Apparatus and method for detection of metal IONS and peas pollutants in a sample

Abstract

The disclosure describes a method and device that can quickly detect metal ion or per- and polyfluoroalkyl substances (PFAS) with high sensitivity and specificity in an easy-to-use and low- cost manner. The method and device utilize bio-nanosensors comprising carbon nanotubes, where binding of ligands on the carbon nanotubes to a target metal ion or PFAS generates electrical signals (e.g., voltage) that are processed and analyzed to detect the absence or presence of the metal ion or PFAS. The method and device do not require extraction of the target metal ion or PFAS from samples, although extracted samples can also be used. A sample, such as an environmental sample (e.g., a water sample, wastewater, agricultural soils, sediments from rivers), which may optionally be diluted in an aqueous solvent such as water (e.g., purified water), is simply added to the bio-nanosensors. The method and device can also quantify the level of the target metal ion or PFAS in the sample. The portable device can be used at a point of care (e.g., at a residential vs commercial property) or in the field (e.g., at a water facility).

Description

APPARATUS AND METHOD FOR DETECTION OF METAL IONS AND PFAS POLLUTANTS IN A SAMPLECross-Reference to Related Applications

[0001] This application claims priority to U.S. Provisional Application Ser. No. 63 / 613,163 filed December 21, 2023, which is incorporated by reference herein in its entirety.Sequence Listing

[0002] This application contains a computer readable Sequence Listing which has been submitted electronically in XML format with this application, and is incorporated herein by reference in its entirety. The Sequence Listing XML file submitted with this application was created on December 12, 2024; is named “MDX0013PCT.xml” and is 6,604 bytes in size. The Sequence Listing does not go beyond the disclosure in the application as filed.Background of the Disclosure

[0003] Environmental contamination by metal ions and per- and polyfluoroalkyl substances (PFAS) poses significant risks to human health and ecosystems. Metal ions, such as lead, mercury, and cadmium, are toxic even at low concentrations, causing adverse effects ranging from neurological damage to organ failure. Similarly, PFAS, often referred to as "forever chemicals," are persistent pollutants linked to severe health conditions, including cancer, immune system impairment, and developmental issues. The growing awareness of these contaminants has heightened the need for effective detection methods to monitor and mitigate their presence in environmental and industrial samples.

[0004] Existing methods for detecting metal ions and PFAS often rely on laboratory -based techniques, such as atomic absorption spectroscopy (AAS), inductively coupled plasma mass spectrometry (ICP- MS), and liquid chromatography -mass spectrometry (LC-MS). While these techniques provide high sensitivity and accuracy, they are typically time-consuming, expensive, and require specialized equipment and trained personnel. Moreover, the sample preparation processes for these methods, including extraction and purification, are labor-intensive and not suitable for rapid, on-site testing, limiting their practical application in scenarios where real-time monitoring is critical.

[0005] To address these limitations, there is a growing need for portable, cost-effective, and user- friendly technologies that enable rapid detection of metal ions and PFAS in various settings.Summary of the Disclosure

[0006] The disclosure describes a method and device that can quickly detect metal ion or per- and polyfluoroalkyl substances (PFAS) with high sensitivity and specificity in an easy-to-use and low- cost manner. The method and device utilize bio-nano sensors comprising carbon nanotubes, where binding of ligands on the carbon nanotubes to a target metal ion or PFAS generates electrical signals (e.g., voltage) that are processed and analyzed to detect the absence or presence of the metal ion or PFAS. The method and device do not require extraction of the target metal ion or PFAS from samples, although extracted samples can also be used. A sample, such as an environmental sample (e.g., a water sample), which may optionally be diluted in an aqueous solvent such as water (e.g., purified water), is simply added to the bio-nanosensors. The method and device can also quantify the level of the target metal ion or PFAS in the sample. Additional testing with one or more other methods can be performed to confirm the presence of metal ion or PFAS. The portable device can be used at a point of care (e.g., at a residential vs commercial property) or in the field (e.g., at a water facility).

[0007] In one aspect, provided is a method for detecting absence or presence of a metal ion or per- and polyfluoroalkyl substances (PFAS). In some embodiments, the method comprises contacting a bio-nanosensor with a sample, wherein the bio-nanosensor comprises carbon nanotubes, ligands associated with or bound to an outer surface of the carbon nanotubes, and the ligands bound specifically to a target metal ion or PFAS; measuring a change in voltage (AV) resulting from contacting the bio-nanosensor with the sample (test AV); comparing the test AV to a reference AV resulting from contacting a bio-nanosensor with a sample known not to have the metal ion or PFAS; and determining absence or presence of the metal ion or PFAS based on comparing the test AV to the reference AV.

[0008] In some embodiments, the reference AV is determined from contacting a plurality of bio- nanosensors with a plurality of samples known not to have the metal ion or PFAS.

[0009] Further provided is a method of for detecting absence or presence of a metal ion or PFAS, which comprises contacting a plurality of (e.g., about 4, 8, 16, 32, 64 or more) bio-nanosensors with the sample; comparing the test AV generated by each of the bio-nanosensors to the reference AV; and determining presence of the metal ion or PFAS if comparing the test AV to the referenceAV for a majority of the bio-nanosensors indicates presence of the metal ion or PFAS, or determining absence of the metal ion or PFAS if comparing the test AV to the reference AV for a majority or half of the bio-nanosensors indicates absence of the metal ion or PFAS.

[0010] In some embodiments, a plurality of different ligands bind specifically to a plurality or panel of (e.g., about 2-10, or about 3, 4, 5, 6 or 7) different target metal ion or PFAS to determine absence or presence of a metal ion or a PFAS, whether in the same test or separate tests.

[0011] In some embodiments, PFAS comprises perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA).

[0012] In some embodiments, the ligands are selected from (2-Hydroxypropyl)-beta-cyclodextrin (HP|3CD; CAS Reg. No. 128446-35-5); Tetramethyl thiuram disulfide (CAS Reg. No. 137-26-8); Disodium bathocuproine Di sulfonate hydrate (CAS Reg. No. 52698-84-7); Tetra thiomolybdate (CAS Reg. No. 15060-55-6); Diethylenetriaminepentaacetic acid (DTPA; CAS Reg. No. 67-43- 6); Poly (diallyl dimethylammonium chloride) (PDAD; CAS Reg. No.26062-79-3); Chitosan (CAS Reg. No. 9012-76-4); Ammonium bicarbonate (CAS Reg. No.1066-33-7); Hexadecyltrimethylammonium bromide (CTAB; CAS Reg. No. 57-09-0); Calcium Hydroxide (CAS Reg. No. 1305-62-0); Nucleotide Aptamers; Copper Phthalocyanine Derivative; Bovine Serum albumin (CAS Reg. No. 9048-46-8); Decafluorobiphenyl (DFB; CAS Reg. No. 434-90-2); potassium carbonate (K2CO3; CAS Reg. No. 584-08-7); decafluorobiphenyl (DFB; CAS Reg. No. 434-90-2); Liver fatty acid-binding protein (L-FABP); Human hemoglobin (Hb; CAS Reg. No. 9008-02-0); Cyclodextrin; a-Cyclodextrin (a-CD; CAS Reg. No.10016-20-3) and P-Cyclodextrin (P-CD; CAS Reg. No. CAS 7585-39-9).

[0013] In some embodiments, the sample is an environmental sample.

[0014] In some embodiments, the sample a water sample from a water-treatment facility (e.g., a sample of wastewater or treated water), an industrial facility (e.g., a sample of wastewater), a business, a domestic residence, a body of water (e.g., a lake, a river or a stream), or a pool of water (e.g., a puddle).

[0015] In another aspect, provided is a method for detecting absence or presence of a metal ion or PFAS, which comprises quantifying the level of the target metal ion or PFAS in the sample based on the test AV. In some embodiments, the level of the target metal ion or PFAS in the sample is calculated using an equation (e.g., a substantially linear equation) formulated from changes in voltage resulting from contacting a plurality of bio-nanosensors with a plurality of samples containing known levels of the target metal ion or PFAS.Brief Description of the Drawings

[0016] A better understanding of features and advantages of the present disclosure will be obtained by reference to the following detailed description, which sets forth illustrative embodiments of the disclosure, and the accompanying drawings.

[0017] Fig. 1. Theoretical view of physical adsorption of ligands to MWCNT, with the metal ions attached to ligands.

[0018] Figs. 2A-C show various ligands for metal ions. Fig. 2A: Detailed view of Hydroxypropyl-beta-cyclodextrin, with lone pairs displayed to demonstrate where binding sites would be located. Fig. 2B: Detailed view of Thiram, with lone pairs displayed to demonstrate where binding sites would be located. Fig. 2C: Theoretical display of how lead would bind to sulfur or oxygen binding sites.

[0019] Figs 3A and 3B show the selective detection of PFOS and PFOA using a single ligand. Fig 3A: Various concentrations of H[3CD for PFOS and PFOA detection. AV is used as a statistical parameter for data computation and analysis. Fig 3B: H(3CD for the detection of various amounts of PFOS and PFOA. Normalized AV is employed for data computation and analysis.

[0020] Figs. 4A and 4B show the selective detection of PFOS and PFOA using different ligands. Fig. 4A: signal strength of PFOS and PFOA at various concentrations. AV / R is used as a statistical parameter for analysis. Fig. 4B: different ligands for PFOS vs. PFOA. Normalized AV is employed for data computation and analysis.

[0021] Fig. 5 shows the differentiation of PFOS / PFOA from metals using the same ligand. Normalized AV is employed for data computation and analysis.

[0022] Fig. 6 shows the testing method of the diclosed metal ions and PFAS device. Fig 6 depicts the testing method for the disclosed metal ions and PFAS detection device.

[0023] Fig. 7 shows the detection metal ion using HP0CD as the ligand.

[0024] Figs. 8A-E show the test data using the disclosed metal ions and PFAS detection device. Fig. 8A: Validated of lead data generated by the device disclosed herein: ICP-MS validation test on 15 PPB lead sample. Fig. 8B: 12-15 MD's validation test on 15 PPB lead sample. Fig. 8C: Data interpretation intervals. Fig. 8D: EPA approved instrument data vs the data generated by the device disclosed herein. Fig. 8E: phosphate test data using PDAD as the ligand. Fig. 8A and 8E: AV is used for data computation and analysis.

[0025] Figs. 9A-E show the detection of PFOS in various water samples. Fig. 9A: detection of PFOS of various concentration as indicated by AV in distilled water. Fig. 9B: detection of PFOS of various concentration. Fig. 9C: detection of PFOS in tap water using HP(3CD. Fig. 9D: PFOS in Connecticut tap and well water. Fig. 9E: EPA 537.1 method LCMSMS result. Fig. 9A-9D: AV is employed for data computation and analysis.

[0026] Figs. 10A-D show the validation of the device disclosed herein in PFOS detection. Fig. 10A: amounts of PFOS in tap water samples. Fig. 10B: detection of PFOA of various concentration as indicated by AV in ultr a pure water. Fig. 10C: detection of PFOA of various concentration as indicated by AV in ultra pure water. Fig. 10D: detection of PFOA of in Haven tap water and ultra-pure water. Fig. 10A and 10D: Normalized AV is used for data computation and analysis. Fig. 10B and 10C: AV is employed for data computation and analysis.Detailed Description of the Disclosure

[0027] While various embodiments of the present disclosure are described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications and changes to, and variations and substitutions of, the embodiments described herein will be apparent to those skilled in the art without departing from the disclosure. It is understood that various alternatives to the embodiments described herein, including materials and methods similar or equivalent to those described herein, may be employed in practicing the disclosure. It is also understood that every embodiment of the disclosure may optionally be combined with any one or more of the other embodiments described herein which are consistent with that embodiment.

[0028] Where a combination is disclosed, it is understood that each possible subcombination of the elements of that combination is also disclosed. Conversely, where different elements or groups of elements are individually disclosed, combinations thereof are also disclosed.

[0029] Where elements are presented in list format or as alternative members of a group (e.g., a Markush group), it is understood that each possible subgroup of the elements is also disclosed, and any one or more elements can be removed from that list or group.

[0030] Where a range of values is recited, it is understood that each intervening integer value and each fraction thereof, as well as each subrange, between the recited upper and lower limits of that range are specifically disclosed. Where a value has an inherent limit, that inherent limit isspecifically disclosed. Where a value is explicitly recited, it is understood that values which are about the same as the recited value are specifically disclosed.

[0031] It is also understood that, unless clearly indicated to the contrary, in any method described or claimed herein that includes more than one act or step, the order of the acts or steps of the method is not necessarily limited to the order in which the acts or steps of the method are recited, but the disclosure encompasses embodiments in which the order is so limited.

[0032] It is further understood that, in general, where an embodiment in the description or the claims is referred to as comprising one or more features, the disclosure also encompasses embodiments that consist of, or consist essentially of, such feature(s).

[0033] It is also understood that any embodiment of the disclosure, e.g., any embodiment found within the prior art, can be explicitly excluded from the claims, regardless of whether or not the specific exclusion is recited in the specification.

[0034] Headings are included herein for reference and to aid in locating certain sections. Headings are not intended to limit the scope of the embodiments and concepts described in the sections under those headings, and those embodiments and concepts may have applicability in other sections throughout the entire disclosure.

[0035] All patent literature and all non-patent literature cited herein are incorporated herein by reference in their entirety to the same extent as if each patent literature or non-patent literature were specifically and individually indicated to be incorporated herein by reference in its entirety.Definitions

[0036] Unless defined otherwise or clearly indicated otherwise by their use herein, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. Singleton el al., Dictionary of Microbiology and Molecular Biology, 2nd Ed., John Wiley and Sons, New York (1994), and Hale and Marham, The Harper Collins Dictionary of Biology, Harper Perennial, New York (1991), provide dictionary definitions of many terms used in the biotechnology ait.

[0037] As used in the specification and the appended claims, the indefinite articles “a” and “an” and the definite article “the” can include plural referents as well as singular’ referents unless specifically stated otherwise or the context clearly indicates otherwise.

[0038] The terms “or / and” and “and / or” mean “either ... or ... , or both ... and ...” when referring to two elements, and mean “either ... , ... or ... , or any combination or all thereof’ when referringto three or more elements. As an example, the phrase “A or / and B” means “either A or B, or both A and B”, and the phrase “A, B or / and C” means “either A, B or C, or any combination or all thereof’.

[0039] As used in the specification and the claims, all transitional terms such as “comprising”, “containing”, “having”, “including”, “composed of’, and the like are open-ended and inclusive, that is, mean including but not limited to and do not exclude additional, unrecited element(s) or method step(s). Only the transitional term “consisting of’ is closed, that is, excludes any additional, unrecited element or method step, and the transitional term “consisting essentially of’ is semi-closed, that is, only allows inclusion of additional, unrecited element(s) or method step(s) that do not materially affect the basic and novel characteristic(s) of that particular embodiment.

[0040] The term “exemplary” as used herein means “serving as an example, instance or illustration”. Any embodiment or feature characterized herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or features.

[0041] The term “about” or “approximately” means an acceptable error for a particular value as determined by one of ordinary skill in the art, which depends in part on how the value is measured or determined. In certain embodiments, the term “about” or “approximately” means within one standard deviation. In some embodiments, when no particular margin of error (e.g., a standard deviation to a mean value given in a chart or table of data) is recited, the term “about” or “approximately” means that range which would encompass the recited value and the range which would be included by rounding up or down to the recited value as well, taking into account significant figures. In certain embodiments, the term “about” or “approximately” means within 10% or 5% of the specified value. Whenever the term “about” or “approximately” precedes the first numerical value in a series of two or more numerical values or in a series of two or more ranges of numerical values, the term “about” or “approximately” applies to each one of the numerical values in that series of numerical values or in that series of ranges of numerical values.

[0042] Whenever the term “at least” or “greater than” precedes the first numerical value in a series of two or more numerical values, the term “at least” or “greater than” applies to each one of the numerical values in that series of numerical values.

[0043] Whenever the term “no more than” or “less than” precedes the first numerical value in a series of two or more numerical values, the term “no more than” or “less than” applies to each one of the numerical values in that series of numerical values.

[0044] The term “ligand” refers to an ion, chemical, compound, molecule, or molecular entity with a functional group that specifically binds to a target molecule to form a coordination complex. In some embodiments, a ligand is capable of forming a stable and specific interaction with a target, which is a metal ion or a per- and polyfluoroalkyl substance (PFAS). In some embodiments, a ligand binds specifically to a metal ion or PFAS. In some embodiments, a ligand binds specifically to a target metal ion or PFAS. In some embodiments, a ligand binds specifically to a target, which is a metal ion or PFAS.

[0045] The term “subject” refers to an animal, including a mammal, such as a primate (e.g., a human, a chimpanzee or a monkey), a rodent (e.g., a rat, a mouse, a gerbil or a hamster), a lagomorph (e.g., a rabbit), a swine (e.g., a pig), an equine (e.g., a horse), a canine (e.g., a dog) or a feline (e.g., a cat). The terms “subject” and “patient” may be used interchangeably herein in reference to a subject / patient (e.g., a mammalian subject / patient such as a human subject / patient) having a medical condition.

[0046] The term “polynucleotide” refers to a polymer composed of nucleotide units. Polynucleotides can contain naturally occurring nucleic acids (e.g., deoxyribonucleic acid [“DNA”] and ribonucleic acid [“RNA”]), or / and nucleic acid analogs. Polynucleotides containing one or more nucleic acid or nucleotide analogs are sometimes called “aptamers”. Nucleic acid or nucleotide analogs include without limitation those which have a non-naturally occurring base / nucleobase, have a sugar or non-sugar moiety other than 2’ -deoxyribose or ribose, or engage in linkages with other nucleotides other than the naturally occurring phosphodiester bond, or a combination or all thereof. Non-limiting examples of nucleic acid or nucleotide analogs include xeno(biotic) nucleic acids (XNAs) having a backbone other than the naturally occurring sugarphosphate backbone present in DNA or RNA (e.g., 2’-O-substituted ribonucleotides [e.g., 2’ 0- methyl ribonucleotides and 2’-O-(2-methoxyethyl) ribonucleotides], cyclohexene nucleic acids [CeNAs], 2’ -deoxy-2’ -fluoroarabino nucleic acids [FANAs], glycol nucleic acids [GNAs], 1,5- anhydrohexitol nucleic acids [HNAs], locked nucleic acids [LNAs] (also called bridged nucleic acids [BNAs]), morpholino nucleic acids [MNAs], peptide nucleic acids [PNAs], and threose nucleic acids [TNAs]), 5-methylcytosine, 5-methyluracil, phosphorothioates / thiophosphates, phosphorodithioates, phosphoramidates, phosphorodiamidates, boranophosphates, phosphorotriesters, methylphosphonates, chiral-methyl phosphonates, and the like. DNA and RNA polynucleotides can be synthesized using a DNA or RNA polymerase or an automated DNA or RNA synthesizer. Polynucleotides containing nucleic acid analogs can be synthesized using,e.g., an engineered DNA or RNA polymerase that recognizes the nucleic acid analogs, a phosphoramidite strategy, or an automated peptide synthesizer in the case of PNAs. The term “nucleic acid molecule” typically refers to a larger polynucleotide. The term “oligonucleotide” typically refers to a shorter polynucleotide. In certain embodiments, an oligonucleotide contains no more than about 100 nucleotides. In some embodiments, when a polynucleotide sequence is represented by a DNA sequence (i.e., A, T, G, C), this also includes the corresponding, or the complementary, RNA sequence (i.e., A, U, G, C) in which “U” replaces “T”.

[0047] The term “complementary” refers to the topological compatibility or matching together of interacting surfaces of two polynucleotides. Thus, the two molecules can be described as complementary, and furthermore the contact surface characteristics are complementary to each other. A first polynucleotide is complementary to a second polynucleotide if the nucleotide sequence of the first polynucleotide is identical to the nucleotide sequence of the polynucleotide binding partner of the second polynucleotide. Complementarity of polynucleotides typically refers to C / G and A / T (or U in the case of RNA) base pairings between antiparallel DNA / DNA, DNA / RNA or RNA / RNA sequences. Thus, the polynucleotide whose sequence is 5’-TATAC-3’ is complementary to a polynucleotide whose sequence is 5’-GT ATA-3’. A nucleotide sequence is “substantially complementary” to a reference nucleotide sequence if the sequence complementary to the subject nucleotide sequence is substantially identical to the reference nucleotide sequence.

[0048] The term “metal ions” refers to electrically charged atoms or groups of atoms derived from metals, including transition metals, alkali metals, alkaline earth metals, lanthanides, and actinides. In some embodiments, representative metal ions include, but are not limited to, mercury, cadmium, arsenic, chromium, nickel, copper, and lead. A transition metal (or transition element) is a chemical element in the d-block of the periodic table (groups 3 to 12). In some embodiments, representative transition metals include Scandium (Sc), Yttrium (Y), Titanium (Ti), Zirconium (Zr), Hafnium (Hf), Rutherfordium (Rf), Vanadium (V), Niobium (Nb), Tantalum (Ta), Dubnium (Db), Chromium (Cr), Molybdenum (Mo), Tungsten (W), Seaborgium (Sg), Manganese (Mn), Technetium (Tc), Rhenium (Re), Bohrium (Bh), Iron (Fe), Ruthenium (Ru), Osmium (Os), Hassium (Hs), Cobalt (Co), Rhodium (Rh), Iridium (Ir), Meitnerium (Mt), Nickel (Ni), Palladium (Pd), Platinum (Pt), Darmstadtium (Ds), Copper (Cu), Silver (Ag), Gold (Au), Roentgenium (Rg), Zinc (Zn), Cadmium (Cd), Mercury (Hg), and Copemicium (Cn). Particularly significant metal ions relevant to the present invention are copper, iron, lead, and arsenic.

[0049] EPA’s proposed action level for the metal ions / PFAS analytes (Table 1)

[0050] Hazard Index:GenX PFBS PFNA PFHxSHazard Index value =10 ppt+2000 ppt 10 ppt+9 ppt

[0051] PFAS Analyte lists (Table 2)Method for detecting metal ions and PFAS pollutants

[0052] The disclosure describes a method and device that can quickly detect metal ions and PFAS pollutants with high sensitivity and specificity in an easy-to-use and low-cost manner. The method and device utilize bio-nanosensors (BNS) comprising carbon nanotubes, where binding of ligands (e.g., tetramethyl thiuram disulfide, tetra thiomolybdate, diethylenetriaminepentaacetic, and nucleotide aptamers) on the carbon nanotubes to a target metal ions and PFAS generates electrical signals (e.g., voltage) that are processed and analyzed to detect the absence or presence of the metal ions and PFAS. The method and device do not require extraction of the target metal ions and PFAS from sample, although extracted samples can also be used. A sample, such as an environmental sample (e.g., a water sample), or an agricultural soils sample, which may optionally be diluted in an aqueous solvent such as water (e.g., purified water), is simply added to the bio- nanosensors.

[0053] The method and device can also quantify the level of the target metal ions and PFAS in the sample. Additional testing with one or more other methods can be performed to confirm the results provided by the method and device of the disclosure. The portable device can be used at a point of care (e.g., at a residential vs commercial property) or in the field (e.g., at a water facility).

[0054] Some embodiments of the method relate to a method for detecting absence or presence of a metal ion or per- and polyfluoroalkyl substances (PFAS) comprising:contacting a bio-nanosensor with a sample, and the bio-nanosensor comprises carbon nanotubes, ligands associated with or bound to an outer surface of the carbon nanotubes, and the ligands bound specifically to a target metal ion or PFAS; measuring a change in voltage (AV) resulting from contacting the bio-nanosensor with the sample (test AV); comparing the test AV to a reference AV resulting from contacting a bio-nanosensor with a sample known not to have the metal ion or PFAS; and determining absence or presence of the metal ion or PFAS based on comparing the test AV to the reference AV.

[0055] In some embodiments, a change in voltage (AV) results from contacting the bionanosensor with the test sample (test AV) is measured, the test AV is compared to a reference AV, and the absence or presence of the metal ion or PFAS is determined based on comparing the test AV to the reference AV.

[0056] In some embodiments, the reference AV is determined from contacting a plurality of bio- nanosensors with a plurality of samples known not to have the metal ion or PFAS.

[0057] In some embodiments, the test AV results from binding between the ligands and the target metal ion or PFAS is greater than the reference AV, optionally by a certain absolute amount (e.g., by at least about 0.015 or 0.020 mV, or by at least about 0.02 V) or by a certain relative amount (e.g., by at least about 20%, 30%, 50% or 100%), indicating presence of the metal ion or PFAS.

[0058] In some embodiments, the test AV results from binding between the ligands and the target metal ion or PFAS is less than the reference AV, optionally by a certain absolute amount (e.g., by at least about 0.015 or 0.020 mV, or by at least about 0.02 V) or by a certain relative amount (e.g., by at least about 20%, 30%, 50% or 100%), indicating presence of the metal ion or PFAS.

[0059] In some embodiments, the test AV is substantially similar to (e.g., within about 10% or 20% of) the reference AV, indicating absence of the metal ion or PFAS.

[0060] In some embodiments, the method comprises: contacting a plurality of (e.g., about 4, 8, 16, 32, 64 or more) bio-nanosensors with the sample; comparing the test AV generated by each of the bio-nanosensors to the reference AV; and determining presence of the metal ion or PFAS if comparing the test AV to the reference AV for a majority of the bio-nanosensors indicates presence of the metal ion or PFAS, or determining absence of the metal ion or PFAS if comparing the test AV to the referenceAV for a majority or half of the bio-nanosensors indicates absence of the metal ion or PF AS.

[0061] In some embodiments, the ligands are a plurality of a particular ligand.

[0062] In some embodiments, the ligands are a plurality of two or more different ligand binding specifically to a particular target metal ion or PFAS.

[0063] In some embodiments, the ligands are a plurality of two or more different ligands binding specifically to two or more different target metal ion or PFAS.

[0064] In some embodiments, using a plurality of different ligands binding specifically to a plurality or panel of (e.g., about 2-10, or about 3, 4, 5, 6 or 7) different target metal ion or PFAS to determine absence or presence of a metal ion or a PFAS, whether in the same test or separate tests.

[0065] In some embodiments, the target metal ion comprises lead, copper and phosphate. In some embodiments, the PFAS comprises perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA).

[0066] In some embodiments, the ligands are selected from (2-Hydroxypropyl)-beta- cyclodextrin (HPPCD; CAS Reg. No. 128446-35-5); Tetramethyl thiuram disulfide (CAS Reg. No. 137-26-8); Disodium bathocuproine Di sulfonate hydrate (CAS Reg. No. 52698-84-7); Tetra thiomolybdate (CAS Reg. No. 15060-55-6); Diethylenetriaminepentaacetic acid (DTPA; CAS Reg. No. 67-43-6); Poly (diallyl dimethylammonium chloride) (PDAD; CAS Reg. No.26062-79- 3); Chitosan (CAS Reg. No. 9012-76-4); Ammonium bicarbonate (CAS Reg. No.1066-33-7); Hexadecyltrimethylammonium bromide (CTAB; CAS Reg. No. 57-09-0); Calcium Hydroxide (CAS Reg. No. 1305-62-0); Nucleotide Aptamers; Copper Phthalocyanine Derivative; Bovine Serum albumin (CAS Reg. No. 9048-46-8); Decafluorobiphenyl (DFB; CAS Reg. No. 434-90-2); potassium carbonate (K2CO3; CAS Reg. No. 584-08-7); decafluorobiphenyl (DFB; CAS Reg. No. 434-90-2); Liver fatty acid-binding protein (L-FABP); Human hemoglobin (Hb; CAS Reg. No. 9008-02-0); Cyclodextrin; a-Cyclodextrin (a-CD; CAS Reg. No.10016-20-3); P-Cyclodextrin (P~ CD; CAS Reg. No. CAS 7585-39-9); human serum albumin; ammonium hydrogen carbonate; and ovalbumin (albumin from chicken egg).

[0067] In some embodiments, the nucleotide aptamers consist of the nucleotide sequence set forth in SEQ ID NO: 1-5.

[0068] In some embodiments, the nucleotide aptamers consist of the nucleotide sequence at least 80% identical to nucleotide sequence set forth in SEQ ID NO: 1-5.

[0069] In some embodiments, the ligands are associated with or bound to the outer surface of the carbon nanotubes by van der Waals force.

[0070] In some embodiments, the carbon nanotubes are impregnated with gold nanoparticles and the ligands bound onto the gold nanoparticles.

[0071] In some embodiments, the carbon nanotubes are or comprise single-wall carbon nanotubes (SWCNTs).

[0072] In some embodiments, the SWCNTs have a diameter of about 0.5-5 nm and a length of about 2-30 microns.

[0073] In some embodiments, the carbon nanotubes are or comprise multi-wall carbon nanotubes (MWCNTs).

[0074] In some embodiments, the MWCNTs have: about 2-6 or 6-12 substantially concentric layers of graphene; a diameter of about 5-50 nm or 50-100 nm; and a length of about 2-30 microns.

[0075] In some embodiments, the sample is a water sample.

[0076] In some embodiments, the sample is an environmental sample.

[0077] In some embodiments, the environmental sample is a water sample from a watertreatment facility (e.g., a sample of wastewater or treated water), an industrial facility (e.g., a sample of wastewater), a business, a domestic residence, a body of water (e.g., a lake, a river or a stream), or a pool of water (e.g., a puddle).

[0078] In some embodiments, the environmental sample is or comprises tap water, groundwater, surface water, drinking water, wastewater, agricultural soils, sediments from rivers, lakes, and estuaries, soil from industrial sites, air samples, biota samples, fish and aquatic organisms, wildlife tissues (e.g., liver, blood), human biological samples (e.g., blood serum, breast milk), food samples, packaged food items, agricultural produce, industrial and household products, treated textiles, firefighting foams, cookware, landfill and leachate samples, solid waste landfill leachates, and soil around landfills.

[0079] In some embodiments, the method can detect the absence or presence of the metal ion or PFAS with an accuracy of at least about 90%, 95% or 98%.

[0080] In some embodiments, the bio-nanosensor(s) is / are prepared with the ligands primed on the outer surface of the carbon nanotubes.

[0081] In some embodiments, the method further comprises contacting the bio-nanosensor(s) with the ligands shortly (e.g., within about 3, 5 or 10 minutes) prior to contacting the bio- nanosensor(s) with the sample.

[0082] In some embodiments, the method further comprises subtracting the AV resulting from contacting the bio-nanosensor(s) with the ligands from the test AV resulting from contacting the bio-nanosensor(s) with the sample.

[0083] In some embodiments, the method further comprises quantifying the level of the target metal ion or PFAS in the sample based on the test AV.

[0084] In some embodiments, the level of the target metal ion or PFAS in the sample is calculated using an equation (e.g., a substantially linear equation) formulated from changes in voltage resulting from contacting a plurality of bio-nanosensors with a plurality of samples containing known levels of the target metal ion or PFAS .

[0085] In one aspect, disclosed herein is a method for processing sensor data, which comprises filtering raw signals using a low-pass filter to remove noise; normalizing signal baselines across sensors; calculating AV as the difference between signal points at distinct intervals and normalizing it by resistance (AV / R); and computing the ratio between the signal from sample- added sensors and the control signal (negative control).

[0086] In some embodiments, the method further comprises identifying and excluding outliers based on predefined thresholds; and computing advanced statistical features, including first and second derivatives, signal slope, steepness, and area under the curve (AUC).

[0087] In some embodiments, the AUC is calculated using the ratio between sample and control signals to quantify cumulative signal differences; a peak difference between the maximum and initial signal values is computed for additional analysis.

[0088] In some embodiments, processed data is categorized into positive, negative, or quantitative outcomes based on cutoff conditions.

[0089] In one aspect, disclosed herein is a system for implementing the method of metal ions and PFAS pollutants detection, which comprises a sensor array for data acquisition; a signal processing module for filtering, normalization, and ratio calculations; and a computation engine for deriving advanced statistical features, including AUC and peak analysis, and interpreting results with cutoff conditions.Device for detecting metal ion or PFAS

[0090] The disclosure further describes a device that performs the method for detecting metal ion or PFAS disclosed herein. The device for detecting metal ions and PFAS pollutant is described in the PCT application (International Application No. PCT / US2024 / 045490), which isincorporated by reference herein in its entirety. The entire disclosure relating to the method also applies to the device.

[0091] Some embodiments of the disclosure relate to a device for detecting absence or presence of a metal ion or PFAS, comprising: a biosensor module comprising bio-nanosensors, wherein: each bio-nanosensor comprises carbon nanotubes and graphene; ligand specifically binding to a target metal ion or PFAS are associated with an outer surface of the carbon nanotubes; the bio-nanosensors are configured to generate an analog electrical signal when the ligand on the carbon nanotubes specifically binds to the target metal ion or PFAS; the graphene facilitates thermal and electrical conduction; and the biosensor module is configured to receive or contact a sample; a heater configured to heat the biosensor module to an optimum temperature for binding between the ligands and the target metal ion or PFAS and to maintain the biosensor module at the optimum temperature during testing; a relay configured to control the temperature of the heater and the biosensor module; a thermistor configured to measure the temperature of the heater and the biosensor module; an AD (analog-to-digital) converter configured to convert analog electrical signals from the biosensor module into digital electrical signals; and a microcontroller configured to receive AD-converted electrical signals and data from the biosensor module via the AD converter, to transfer data, to obtain temperature readings from the thermistor, and to control the heater via control of the relay; wherein the metal ion or PFAS detection device is configured to connect to a computer device and to perform the method for detecting absence or presence of metal ion or PFAS described herein.

[0092] The analog electrical signal generated by the bio-nanosensors when the ligand on the carbon nanotubes binds to the target metal ion or PFAS relates to an electrical property such as voltage, electrical conductivity / conductance, electric current flow, or electrical resistivity / resistance. In some embodiments, the electrical signal is signal voltage, and the microcontroller receives voltage vs time data from the biosensor module via the AD converter.

[0093] In some embodiments, the metal ion or PFAS detection device further comprises a connector board electrically connected to the biosensor module and the AD converter andconfigured to receive analog electrical signals from the biosensor module and to transfer the signals to the AD converter.

[0094] In some embodiments, the metal ion or PFAS detection device further comprises a sample-collection module configured to collect a sample and to bring the sample into contact with the bio-nanosensors. The sample can be diluted with an aqueous solvent such as water (e.g., purified water).

[0095] In some embodiments, the computer device comprises: software installed in the computer device or obtained from a remote server or the digital cloud, and configured to provide instructions for operating the metal ion or PFAS detection device and performing the method for detecting a metal ion or PFAS, obtain data (e.g., voltage vs time) from the microcontroller of the metal ion or PFAS detection device, process data (e.g., calculate the change in voltage [AV] between two timepoints), analyze data (e.g., analyze AV data for trends or patterns), and formulate and provide results; a memory coupled to the software and configured to store information, data and instructions; and a processor coupled to the software and the memory and configured to execute instructions and operations, to process and analyze data, and to perform the method for detecting a metal ion or PFAS.

[0096] In some embodiments, data processing or / and data analysis is / are performed by the software in the digital cloud. In further embodiments, instructions and operations provided by the software are executed by a processor in the metal ion or PFAS detection device.

[0097] The computer device can be any suitable computer device for communicating with and operating the metal ion or PFAS detection device. In some embodiments, the computer device is a laptop computer, a desktop computer, a tablet or a smartphone.

[0098] The metal ion or PFAS detection device can be connected to the computer device via a cable, such as a USB Type-A or Type-C cable. Alternatively, the metal ion or PFAS detection device can be connected to the computer device via a wireless connection, such as Wi-Fi or Bluetooth.

[0099] In some embodiments, the optimum temperature for testing is about 45-70 °C, 50-70 °C or 55-65 °C. In further embodiments, the optimum temperature for testing is about 50-55 °C, 55- 60 °C or 60-65 °C. In certain embodiments, the optimum temperature for testing is about 55 °C, 60 °C or 65 °C.

[0100] In some embodiments, the carbon nanotubes are or comprise single-wall carbon nanotubes (SWCNTs). The SWCNTs can have any suitable dimensions. In certain embodiments, the SWCNTs have a diameter of about 0.5-5 nm and a length of about 2-30 microns. In certain embodiments, the SWCNTs have a purity of at least about 95% or 98%.

[0101] In other embodiments, the carbon nanotubes are or comprise multi-wall carbon nanotubes (MWCNTs). The MWCNTs can have any suitable number of walls. In certain embodiments, the MWCNTs have about 2-6 or 6-12 substantially concentric layers of graphene. The MWCNTs can have any suitable dimensions. In certain embodiments, the MWCNTs have a diameter of about 5- 50 nm or 50-100 nm and a length of about 2-30 microns. In certain embodiments, the MWCNTs have a purity of at least about 95% or 98%.

[0102] In some embodiments, the graphene of each bio-nanosensor is a multi-layer graphene or nano-graphite platelet comprising about 5-30 layers of graphene, or about 5-10, 10-20 or 20-30 layers of graphene.

[0103] The biosensor module can contain any suitable number of bio-nanosensors for detecting one or more metal ion or PFAS. In some embodiments, the biosensor module comprises about 4, 8, 16, 32, 64, 128, 256, 512, or more bio-nanosensors. In some embodiments, a disposable cartridge may be used. Additional sensor allows the user to test significantly more and different analytes simultaneously from a single sample in a single test run.

[0104] In another alternative embodiment, the sample can be added from a single syringe and then the liquid will be flowing onto the sensor cartridge. The syringe may have a filter for filtering out solid particles in the sample and a water-ball for diluting the sample. The syringe may be disposable. The syringe may also have a heating step where the sample can be pre-heated if the user required by a heater coil in the reader unit. The syringe may be operated mechanically by a stepper motor system.

[0105] To avoid sample contamination, the biosensor module and the optional sample-collection module can be single-use / disposable.

[0106] In certain embodiments, the biosensor module is manufactured with the ligand bound on the outer surface of the carbon nanotubes. In other embodiments, the ligands are added to the bio- nanosensors shortly (e.g., within about 3, 5 or 10 minutes) prior to addition of the sample to the bio-nanosensors.

[0107] In some embodiments, the software sets a threshold value of AV (e.g., about 0.015 or 0.020 mV, or about 0.02 V) between two (e.g., pre-determined) timepoints equal to or above whichthe AV calculated for a particular bio-nanosensor is deemed a positive reading and below which the AV is deemed a negative reading, and the software determines a positive result (presence of a metal ion or PF AS) if the majority of the bio-nanosensors provide positive readings or a negative result (absence of a metal ion or PFAS) if the majority or half of the bio-nanosensors provide negative readings.

[0108] In some embodiments, the metal ion or PFAS detection device further comprises a lightemitting diode (LED) which indicates a qualitative result of the test by a different color of light, such as a red light for a positive result, a green light for a negative result, or a yellow or orange light for an inconclusive result. In certain embodiments, the microcontroller controls the LED.

[0109] In alternative embodiments, the metal ion or PFAS detection device can comprise a temperature control circuit, such as a MOSFET (Metal Oxide Semiconductor Field Effect Transistor) switching device for turning a heating circuit or a cooling fan on and off in order to control the temperature of the device while in use. In a related alternative embodiment, the sensor material itself can be used as the heating device.

[0110] In additional embodiments, the computer device provides a qualitative result of the test, and optionally a quantitative result of the test (e.g., the level of the target metal ion or PFAS in the sample), on the computer device, such as on the screen or in a Results page or file of the computer device.

[0111] In further embodiments, the computer device provides a qualitative result of the test, and optionally a quantitative result of the test, to the subject providing the sample if a biological sample and the person overseeing the test, and optionally a government or health authority, agency or department if reporting of such result(s) thereto is required.

[0112] In some embodiments, the metal ion or PFAS detection device is capable of providing a qualitative result or / and a quantitative result of the test within about 15 or 20 minutes after addition of the sample to the biosensor module.

[0113] The metal ion or PFAS detection device can be plugged into an electrical socket. Alternatively, the metal ion or PFAS detection device can be powered by a battery, which enables use of the portable metal ion or PFAS detection device in the field.

[0114] The metal ion or PFAS detection device can be used at a point of care, such as at a watertreatment facility (e.g., a sample of wastewater or treated water), an industrial facility (e.g., a sample of wastewater), a business, a domestic residence, a body of water (e.g., a lake, a river or a stream), or a pool of water (e.g., a puddle).

[0115] The embodiments above, as well as others, may be combined such that the device and system may be designed as a high throughput system that can analyze a large number of samples for the same analyte in 5-30 minutes. In a high throughput configuration, the number of sensors is generally from 8 to 96 or more. A printed circuit board on which the sensors are attached can contain the disposable heating elements and the MOSFET system described above can control the temperatures of each sensor. In another embodiment, some of the sensors can be primed and heated to a different temperature so that testing can be performed for multiple samples for 2 or more analytes simultaneously. All samples will be separately pipetted on to the sensors mechanically. Samples can be collected and added to disposable vials with a QR code printed. The high throughput device can have a preheating step where the sample vials will be pre-heated after the QR-code has been read by the software. The adding of sample to the sensor can be performed 8 at a time or any multiples of 8. The software will start gathering data as soon it has detected that there is sample on the senor surface.

[0116] In some embodiments, the device disclosed herein for measuring the concentration of metal ions and PFAS comprises carbon nanotubes (CNTs). The CNTs in the bio-nanosensors (BNS) can be functionalized with ligands or receptors that bind to ions or other metal ions in water selectively. The device disclosed herein is able to differentiate the adsorption of various ions / chemicals based on the size, charge, and electronic configuration of the metal, as well as the CNT property.

[0117] In some embodiments, single analyte detection by the metal ions and PFAS detection device is disclosed herein. Functionalized BNS sensor serves as versatile sensors capable of detecting a variety of substances, including PFAS analytes (Table 2), lead, arsenic, iron, manganese, copper, turbidity, pH, hardness, alkalinity, nitrate as N, nitrite as N, chloride, sulfate, antimony, phosphate, barium, beryllium, cadmium, chromium (total), nickel, selenium, silver, thallium, mercury, and fluoride. Designed ligands for individual analytes assure adsorption that is selective, thereby reducing interference from other ions. For example, PFAS compounds are selectively targeted with ligands that possess a strong affinity, resulting in negligible crossreactivity. The most significant contaminant among the PFAS (per- and polyfluoroalkyl substances) class of compounds is PFOS (perfluorooctane sulfonate) and PFOA (perfluorooctanoic acid). The chemical structure and the hazard index of these compounds are provided later. As an example of PFAS compounds we have tested, validated and are demonstrating the results of PFOS and PFOA.

[0118] By leveraging the adaptability of carbon nanotubes, the device disclosed herein not only detects but also evaluates a wide range of water quality parameters that are vital for assessing public health and the environment. This approach offers a comprehensive solution for monitoring a wide range of contaminants by calibrating the sensors to establish concentration-response relationships. As a result, it has the potential to be utilized in water quality management and regulatory compliance. The versatility and effectiveness of this methodology are highlighted by its capacity to detect multiple analytes selectively; this provides a promising avenue for the advancement of water quality monitoring practices.

[0119] In some embodiments, multi analyte detection by the metal ions and PFAS detection device is disclosed herein. A multi-analyte panel for simultaneous detection of various metal ions and PFOS in one test streamlines laboratory workflows, saving time and resources. This efficient approach provides a comprehensive overview of water quality, allowing for early contamination warnings and rapid response strategies. Its adaptability to different settings, including field applications, enhances its versatility, making it a powerful tool for real-time environmental monitoring and informed decision-making. The consolidated analysis in a single test promotes sustainability and cost-effectiveness, making the multi-analyte panel a valuable asset in advancing efficient and holistic water quality assessment.

[0120] Methods to attach ligands to CNT-Graphene are disclosed herein. The current method of attaching ligands to CNT-Graphene involves non-covalent functionalization, which comprises selecting ligands, dispersing CNT-Graphene in IPA, printing on a substrate and adding ligands. The current alternative method to attaching ligands to CNT-Graphene involves polymer wrapping, which comprises dissolving the polymer in IPA to create a polymer solution, mixing the CNT- Graphene with the polymer solution and subjecting the mixture to ultrasound using sonication. The resulting CNT-Graphene-polymer composite can be printed on a substrate. Another current alternative method to attaching ligands to CNT-Graphene involves covalent functionalization, which involves selecting ligand, and activating of CNT with reactive groups. Attaching of ligand using coupling agent and printing on substrate.

[0121] In some embodiments, the selectivity of ligands is disclosed. The principle of “like prefers like” applies to the ligand and the metal atom / ion it is trying to attach to. To simplify the trend and identification of the properties, the following labels are used. Regarding hard ligands, the electron orbitals are tightly bounded, decreasing the radii size as the periodic table progresses from left to right. Because of that, they have a “higher surface charge density and a tendencytowards ionic character” (e.g., F-). Regarding soft ligands, due to less electron presence on the valence shell, the orbitals have a larger size (Fig. 1).Ligands for the Detection of Metal Ions and PFAS Pollutants

[0122] Ligands for Lead detection include but are not limited to HPpCD (2-Hydroxypropyl)- beta-cyclodextrin and Tetramethyl thiuram disulfide.

[0123] In some embodiments, HPpCD (2-Hydroxypropyl)-beta-cyclodextrin (CAS Reg. No. 128446-35-5) is a current ligand for Lead detection. While the binding sites can always change, the shape of these specific binding sites makes these the most suitable contenders. The trapezoidal shape provides a claw-like ability to chelate the metal ions. The molecule displayed at the bottom (Fig. 2A) is an example of R2O. That binding site can be assumed less desirable than the ROH (displayed on top) due to the strain on the other carbons. This means that the molecules highlighted in red are most likely to become binding sites. In general, oxygen is a poorer ligand compared to nitrogen and sulfur. Detailed view of HCD, with lone pairs displayed to demonstrate where binding sites would be located (Fig. 2A)

[0124] In some embodiments, Tetramethyl thiuram disulfide (CAS Reg. No. 137-26-8) is an alternative ligand for Lead detection. The lone pairs on the nitrogen and sulfur atoms allow them to act as Lewis bases. They will donate their electrons to the metal ion / atom, binding to one another. There are possible theories as to why Pb2+ will bind to sulfur over nitrogen. The most plausible is the structure interference of the adjacent bonds. The nitrogen is bonded to 3 carbons with approximately 109.5- 120-degree angles between each one (view image on the right). Adding a lead atom to the bottom will repel all the carbon bonds away, causing a strain between each other. The lone pairs on both variations of sulfur are away from all bonds; attaching a lead atom will cause negligible interference. Detailed view of Thiram, with lone pairs displayed to demonstrate where binding sites would be located (Fig. 2B). In aqueous form, the lead forms a coordination shell with the water molecules, developing an octahedral shape. When two sulfur atoms of Thiram interact with the lead ions, each one occupies two of the coordination sites, displacing the water molecules originally there. The oxygen atoms on HP CD bind to the lead via chelation. Based on the chelation activity and the ionic properties of lead, the image on the right was constructed. Theoretical display of how lead would bind to sulfur or oxygen binding sites is shown in Fig. 2C.

[0125] Ligand for Copper detection include but are not limited to Disodium bathocuproine Di sulfonate hydrate, Tetra thiomolybdate, and Diethylenetriaminepentaacetic acid (DTPA).

[0126] In some embodiments, Disodium bathocuproine Di sulfonate hydrate (CAS Reg. No. 52698-84-7) is a current ligand for copper detection. Disodium bathocuproine Di sulfonate hydrate is selective to mainly Cu(I) ions, but it does not mean Cu (II) cannot bind to it. When binding to Cu (II), the nitrogen creates a distorted tetrahedral shape that forces it to reduce to Cu(I).

[0127] In some embodiments, Tetra thiomolybdate (CAS Reg. No. 15060-55-6) is an alternative ligand for copper. Tetra thiomolybdate is an inorganic ligand that is selective towards copper. Since it is inorganic, it can remove phosphate and has a potential cross-reactant.

[0128] In some embodiments, Diethylenetriaminepentaacetic acid (DTPA; CAS Reg. No. 67- 43-6) is another alternative ligand for copper. A polyaminocarboxylate chelator (like EDTA) that has amino nitrogen (selective to copper) and a carboxyl oxygen (selective to all metals).

[0129] In some embodiments, Poly (diallyl dimethylammonium chloride) [PDAD; CAS Reg. No.26062-79-3] is a current ligand for phosphate. Even though PDAD is not a true ligand, it serves the same function for the phosphate ion. Anion Phosphate will be attracted to the positively charged nitrogen.

[0130] The ligands for Perfluorooctanesulfonic acid (PFOS) detection include but are not limited to (2-Hydroxypropyl)-beta-cyclodextrin, PDAD and 2-Hydroxypropyl-P-Cyclodextrin, Chitosan, Carbon Nanotube and Graphene Sensor, Ammonium bicarbonate, Hexadecyltrimethylammonium bromide (CTAB) and Calcium Hydroxide.

[0131] In some embodiments, (2-Hydroxypropyl)-beta-cyclodextrin (HPPCD; CAS Reg. No. 128446-35-5) is a ligand for PFOS detection based on its property to form inclusion complexes, stabilization of the complexes, adsorption of PFOS within the hydrophobic cavity of HPpCD and influence of electrical conductivity. Formation of inclusion complex: the hydrophobic cavity of HPpCD can encapsulate hydrophobic or amphiphilic guest molecules like PFOS. The hydrophobic tails of PFOS can fit inside the cavity of HPpCD, forming an inclusion complex. This complexation occurs due to the hydrophobic interactions between the hydrocarbon tail of PFOS and the inner cavity of HPpCD. Stabilization: once the inclusion complex is formed, the hydrophobic interactions between the PFOS tail and the HPPCD cavity help stabilize the complex. Additionally, the hydroxypropyl groups on the outer surface of HPpCD can interact with the sulfonic acid (SO3H) group of PFOS through hydrogen bonding or other non-covalent interactions. Adsorption: The formation of the inclusion complex effectively "traps" PFOS within the hydrophobic cavity of HPpCD. This leads to the adsorption of PFOS by HPpCD from the solution. The adsorption process reduces the concentration of PFOS in the solution. Electricalconductivity and conductivity changes: the interaction of HPpCD and PFOS complexes with CNTs and graphene could influence their electrical conductivity. Depending on the nature of the interaction, conductivity may increase or decrease. For example, functionalization may introduce dopants or alter charge transfer characteristics and modify the surface of carbon nanotubes and graphene by adsorption or covalent bonding. This modification can introduce new functional groups and alter the chemical and physical properties of the materials.

[0132] In some embodiments, PDAD and 2-Hydroxypropyl-P-Cyclodextrin are the current ligands for PFOS detection. This disclosure utilizes a novel primer composed of Poly (diallyldimethylammonium chloride) (PDAD; CAS Reg. No. 26062-79-3) and 2-hydroxypropyl- P -cyclodextrin (HP-P-CD; CAS Reg. No.128446-35-5) for detecting perfluoro octane sulfonate (PFOS) in aqueous environments using the device disclosed herein. The combination of these compounds applied on a sensor composed of carbon nanotube (CNT) and graphene significantly enhances PFOS detection in terms of sensitivity and selectivity.

[0133] The structure and functionality of PDAD and HP-P-CD chemical are disclosed herein. PDAD (Poly (diallyldimethylammonium chloride)) is a cationic polymer with a repeating unit structure as follows: [-CH2-CH(CH3)-N+(CHs)2-CH2-CF-]n. The positive charge on the quaternary ammonium group (N tCH^h) creates a strong electrostatic interaction with the negatively charged sulfonate group (-SO? ) of PFOS. This leads to efficient attraction and immobilization of PFOS at the sensor surface, enhancing the capture of PFOS molecules from the surrounding water sample. 2-Hydroxypropyl-P-cyclodextrin (HP-P-CD) is a modified cyclic oligosaccharide, with a hydrophilic exterior and a hydrophobic interior cavity that can encapsulate hydrophobic molecules like the fluorinated tail of PFOS. The chemical structure of HP-P-CD is as follows: C42H70O35 + (- OCH2CHOHCH3). The cyclic structure forms a toroidal shape, where the hydroxyl groups on the exterior allow water solubility, and the hydrophobic cavity selectively binds to the fluorocarbon tail of PFOS through host-guest complexation.

[0134] Synergistic detection mechanism relates to the combination of PDAD and HP-P-CD in a mixed primer solution is designed to leverage their distinct chemical properties to detect PFOS more effectively: PDAD contributes by electrostatically attracting the PFOS through its negatively charged sulfonate groups (-SO3 ).

[0135] HP-P-CD selectively binds the hydrophobic tail of PFOS (-CsFn), forming an inclusion complex that stabilizes the interaction and prevents desorption. This dual mechanism ensures that PFOS is efficiently captured and retained on the sensor surface, improving both sensitivity andselectivity in the detection process. The carbon nanotube and graphene composite serve as the conductive matrix for the electrochemical sensor, providing an excellent platform for signal transduction. When PFOS molecules are captured by the PDAD and HP-P-CD primer, their presence causes measurable changes in the electrochemical properties of the CNT / graphene surface. These changes are detected as variations in electrical resistance using the device disclosed herein, with the magnitude of the signal correlating with PFOS concentration. Chemical reaction mechanisms are disclosed herein. Electrostatic attraction is created based on the sulfonate group (-SO3 ) of PFOS interacting electrostatically with the quaternary ammonium group of PDAD: R- SO3 + (PDAD-N+(CH3) 2) to from electrostatic complex. Host-guest complexation is created base on the hydrophobic tail of PFOS encapsulated within the hydrophobic cavity of HP-p-CD: CsFr?- SO3 + HP-P-CD to form HP- -CD- PFOS Complex. Together, these mechanisms ensure that PFOS is captured on the sensor surface, altering its electrochemical properties in a measurable way.

[0136] During the detection process, upon priming the sensor with the PDAD and HP-P-CD mixture, a water sample containing PFOS is introduced to the sensor surface. PFOS molecules are captured through a combination of electrostatic attraction (via PDAD) and host-guest binding (via HP-P-CD). This interaction alters the electrical characteristics of the CNT / graphene transducer, generating a detectable electric signal that corresponds to the concentration of PFOS in the sample.

[0137] Chitosan (CAS Reg. No. 9012-76-4) to detect PFOS in water is disclosed herein. Chitosan, a biocompatible polysaccharide derived from chitin and composed of P-(l— >4)-linked D-glucosamine and N-acetyl-D-glucosamine units, offers an effective ligand due to its unique chemical characteristics. The degree of deacetylation in chitosan provides amino groups (-NH2) that are positively charged under acidic to neutral conditions, allowing strong electrostatic interactions with negatively charged PFOS molecules, particularly the sulfonate group (-SO3 ), enabling effective adsorption onto the sensor surface. Alongside these electrostatic interactions, chitosan also stabilizes PFOS binding through hydrophobic interactions between the fluorinated carbon tail of PFOS and the polysaccharide backbone of chitosan. This dual binding mechanism results in a strong binding affinity (K) and a favorable Gibbs free energy (AG < 0), promoting spontaneous and stable chitosan-PFOS complex formation. This stability enhances detection reliability, as the binding interactions between PFOS and chitosan alter the charge distribution on the sensor surface, detectable as measurable changes in current. The presence of the CNT-graphenecomposite substrate, with its high conductivity, further amplifies these electrochemical signals, enabling enhanced electron transfer and producing a distinct response to PFOS binding.

[0138] The adsorption of PFOS onto chitosan is driven by several key mechanisms. Primarily, electrostatic interactions occur between the positively charged chitosan surface and the negatively charged PFOS molecules. Additionally, hydrophobic interactions play a significant role in the sorption process. This sorption is largely diffusion-controlled, as evidenced by the good fit of the double-exponential model to kinetic data. At higher PFOS concentrations, hemi-micelles and micelles may form within the porous structure of chitosan, further enhancing its sorption capacity. The pore structure and rigidity of the crosslinked chitosan beads also influence the adsorption mechanism, contributing to the overall efficiency of PFOS removal. These combined mechanisms result in chitosan's high sorption capacity for PFOS, with studies reporting capacities up to 5.5 mmol / g at equilibrium concentrations of 0.33 mmol / L, which is significantly higher than some conventional adsorbents.

[0139] This invention combines high selectivity, biocompatibility, and environmental stability, making it a highly suitable and scalable approach for PFOS detection in environmental applications. The accessibility and cost-effectiveness of chitosan, derived from the abundant natural polymer chitin, further strengthens its use in this system.

[0140] In some embodiments, Carbon Nanotube and Graphene Sensor to detect PFOS in water is disclosed herein. Carbon nanotubes are cylindrical structures with exceptional electrical conductivity, mechanical strength, and large surface areas. Graphene, a single layer of carbon atoms arranged in a hexagonal lattice, is known for its excellent electron mobility, high surface area, and chemical stability. When combined, CNT and graphene create a hybrid material with synergistic properties that enhance their sensing performance. The combination of CNT and graphene increases the sensor's sensitivity due to their high surface area and excellent electrical conductivity. Even small amounts of PFOS can disrupt the electrical properties of the sensor, allowing for the detection of low concentrations of PFOS in water. CNTs and graphene provide a fast electron transfer rate, ensuring that changes in the sensor's electrical properties are rapidly detected when PFOS molecules adsorb onto the surface. The chemical and electrochemical stability of CNTs and graphene make them ideal materials for sensors that need to function in aqueous environments over extended periods. They are resistant to chemical degradation and can maintain their properties in various water conditions (e.g., pH, ionic strength). Mechanism of PFOS detection via electrochemical output: Perfluoro octane sulfonate (PFOS), with itshydrophobic fluorinated tail (-CsFi?) and hydrophilic sulfonate group (-SO ), exhibits a dual nature, making it an ideal candidate for electrochemical detection using a CNT-graphene sensor. PFOS interacts with the sensor in two primary ways: 1) electrostatic interaction: the sulfonate group (-SO3 ) of PFOS is negatively charged, which can interact electrostatically with any residual surface charges on the sensor, particularly from defects, impurities, or functional groups that might be present on the CNT or graphene surfaces. 2) hydrophobic interaction: the hydrophobic fluorinated tail of PFOS has a high affinity for the sp2carbon networks of CNT and graphene, where hydrophobic interactions between the carbon materials and the fluorinated chain promote adsorption of PFOS onto the sensor surface.

[0141] When PFOS binds to the sensor, it disrupts the electrical properties of the CNT-graphene composite. This disruption can manifest as changes in the electrical resistance of the sensor, all of which can be measured using the device disclosed herein.

[0142] When PFOS binds to the sensor, it introduces a resistive change in the CNT-graphene network. The sensor measures this change, with the magnitude of the shift in the electric signal correlating with the concentration of PFOS in the water sample. The sensitivity of the sensor is enhanced by the large surface area of the CNT-graphene composite, which allows for the detection of even trace levels of PFOS.

[0143] There are resistance changes in CNT-graphene network for PFOS detection. In some embodiments, the resistance is increased. If the adsorption of PFOS blocks or interferes with charge transport pathways on the CNT-graphene surface, the overall resistance of the sensor may increase. This happens when PFOS molecules occupy active sites that would otherwise facilitate electron transfer. In some embodiments, the resistance is decreased. Conversely, if PFOS facilitates charge transfer through some mechanism (such as a redox reaction), it could lower the resistance.

[0144] In some embodiments, Ammonium bicarbonate to detect PFOS in water is disclosed herein. Ammonium bicarbonate (NH4HCO3NH_4HCO_3NH4HCO3; CAS Reg. No.1066-33-7) can be employed as a functional ligand to enhance PFOS (perfluorooctane sulfonate) detection in water when integrated onto a carbon nanotube (CNT) and graphene sensor surface. This mechanism utilizes the electrostatic affinity between the ammonium bicarbonate and the negatively charged PFOS molecules.

[0145] Electrostatic interaction between ammonium bicarbonate and PFOS is disclosed herein. Upon dissociation in water, ammonium bicarbonate releases ammonium ions(NH4+NH_4A+NH4+) and bicarbonate ions (HCO3-HCO_3A-HCO3-). The positively charged NH4+NH_4A+NH4+ ions create a favorable electrostatic environment for attracting PFOS molecules, which contain a negatively charged sulfonate group (-SO3 ) on the hydrophilic head. This interaction enhances the binding affinity of PFOS to the sensor surface, facilitating effective PFOS capture.

[0146] In some embodiments, adsorption of ammonium bicarbonate on carbon nanotube and graphene surface is disclosed herein. The carbon nanotube and graphene structure provide a high- surface-area substrate that exhibits hydrophobic characteristics, which enhances the physical adsorption of PFOS due to its amphiphilic nature. The hydrophilic sulfonate head of PFOS interacts with the NH4+NH_4A+NH4+ ions from the ammonium bicarbonate, while the hydrophobic perfluorinated tail of PFOS aligns with the hydrophobic CNT and graphene surface, further strengthening the adsorption of PFOS on the sensor.

[0147] Signal transduction is created by binding of PFOS to the ammonium bicarbonate. The binding of PFOS to the ammonium bicarbonate-functionalized CNT and graphene sensor surface leads to measurable changes in the sensor's electrical properties, such as alterations in resistance or conductivity. These changes are detectable and correlate with PFOS concentration, thus allowing the device to function as a sensitive PFOS sensor.

[0148] In some embodiments, reversibility and sensor regeneration for the PFOS detection device are disclosed herein. The soluble nature of ammonium bicarbonate allows for a reversible binding mechanism, enabling the sensor to release PFOS upon washing with water or an appropriate buffer. This reversibility provides the potential for sensor regeneration and repeated use.

[0149] In some embodiments, Hexadecyltrimethylammonium bromide (CTAB) to detect PFOS in water is disclosed herein. Hexadecyltrimethylammonium bromide (CTAB; CAS Reg. No. 57- 09-0) is an effective ligand for detecting PFOS (perfluorooctane sulfonate) in water due to its cationic and amphiphilic properties, which promote strong interactions with PFOS molecules. When applied to a carbon nanotube (CNT) and graphene sensor surface, CTAB can facilitate PFOS detection through the following mechanisms. Electrostatic Interaction: PFOS is an anionic molecule in water due to its sulfonate (-SO3 ) group at the hydrophilic end. This negatively charged head makes PFOS highly receptive to positive charges. CTAB is a quaternary ammonium compound with a positively charged head group, N+(CH3)3, which becomes available in water as CTAB dissociates into cetyltrimethylammonium C16H33N+(CH3)3 and bromide ions. Thepositive charge on the ammonium head of CTAB is attracted to the negative sulfonate head of PFOS, resulting in a strong electrostatic (ionic) interaction. This electrostatic attraction brings the PFOS molecules close to the CTAB on the sensor surface. Hydrophobic Interaction: Both CTAB and PFOS have long hydrophobic tails. PFOS contains a perfluorinated chain, which is highly hydrophobic, while CTAB has a hydrophobic 16-carbon (hexadecyl) chain. When PFOS and CTAB come close together, their hydrophobic tails tend to align, maximizing hydrophobic interactions and reducing the water-exposed surface area. This hydrophobic association strengthens the interaction by promoting a stable arrangement of the molecules on the surface of the carbon nanotube (CNT) and graphene sensor. This arrangement also stabilizes the binding between PFOS and CTAB, helping to keep PFOS close to the sensor surface, where it can be detected.

[0150] In some embodiments, in addition to electrostatic and hydrophobic interactions, there are weaker Van der Waals forces between the hydrophobic regions of PFOS and CTAB. These forces contribute further to the stability of the PFOS-CTAB association on the sensor.

[0151] In some embodiments, synergy with the CNT and Graphene Surface is disclosed herein. The high surface area and conductive nature of the CNT and graphene sensor allow CTAB molecules to uniformly coat the surface. This facilitates optimal exposure of CTAB’s positive charge and hydrophobic tail, enhancing the PFOS capture efficiency through both electrostatic and hydrophobic interactions.

[0152] In some embodiments, Calcium Hydroxide to detect PFOS in water is disclosed herein. Calcium hydroxide (Ca (OH)2; CAS Reg. No. 1305-62-0) can act as a ligand to attract and bind perfluoro octane sulfonate (PFOS) on a carbon nanotube (CNT) and graphene-based sensor. The interaction relies on both electrostatic and ion exchange mechanisms rather than covalent bonding. Here’s a breakdown of the chemistry behind the binding interactions. Electrostatic attraction with calcium ions. In an aqueous environment, calcium hydroxide dissociates into calcium ions (Ca2 +) and hydroxide ions (OH-). PFOS is an anionic compound, with its sulfonate head group (- SO3-) carrying a negative charge. The positively charged Ca2+ ions have a strong affinity for the negatively charged sulfonate groups of PFOS, creating an electrostatic attraction between the Ca2+ ions and PFOS molecules. This interaction pulls PFOS close to the calcium hydroxide-coated sensor surface, enabling effective binding.

[0153] In some embodiments, ion exchange mechanism of calcium hydroxide for PFOS detection in water is disclosed herein. The presence of hydroxide ions (OH-) at the sensor surfacecan facilitate an ion exchange mechanism. In this scenario, PFOS displaces some OH- ions and binds to the calcium ions on the surface. The OH- ions may be replaced by the sulfonate group of PFOS, allowing it to interact more strongly with the calcium on the sensor. This displacement of hydroxide ions helps stabilize PFOS at the sensor surface.

[0154] In some embodiments, hydrophobic interaction with CNT and Graphene Surface is disclosed herein. PFOS has a hydrophobic perfluorinated carbon chain tail, which interacts favorably with the hydrophobic surface of the CNT and graphene. Once the sulfonate group of PFOS is electrostatically attracted to the calcium ions, the hydrophobic tail aligns with the CNT and graphene, enhancing the stability of PFOS on the sensor surface through hydrophobic interactions.

[0155] In some embodiments, formation of calcium sulfonate complexes is disclosed herein. The combination of electrostatic and ion exchange interactions creates a stable calcium-sulfonate complex between the calcium ions and the sulfonate group of PFOS. This complex formation anchors PFOS effectively on the sensor surface without forming covalent bonds. The strength of the calcium-sulfonate complex ensures that PFOS remains bound, facilitating reliable detection on the sensor. The binding of PFOS to calcium ions on the sensor surface induces measurable changes in the electrical conductivity or resistance of the CNT and graphene sensor, allowing for sensitive PFOS detection.

[0156] The ligands for PFOA (Perfluorooctanoic acid) detection include but are not limited to copper phthalocyanine derivative, bovine serum albumin, PDAD and DFB, K2CO3, BCD, and DFB, Nucleotide Aptamers, a-Cyclodextrin, Liver fatty acid-binding protein, and human hemoglobin.

[0157] In some embodiments, copper phthalocyanine derivative is a current ligand for phosphate detection. Certain organometallic frameworks, such as Methylene Blue or chromium phthalate within Metal- Organic Frameworks (MOFs), demonstrate electrochemical sensitivity to Per- and Polyfluoroalkyl Substances (PFAS) compounds. Instances of interaction with nanocarbons, thereby modifying the electronic structure of carbon conductors, render these frameworks suitable as capture reagents for PFAS, with their capture being indicated by resultant changes in Boron Nitride Sheets (BNS) conduction. The inclusion of copper (Cu) in Phthalocyanine, a component of the sensor, is anticipated to enhance responsiveness to PFAS while minimizing responses to metals present in natural water or soil samples.

[0158] In some embodiments, bovine serum albumin (CAS Reg. No. 9048-46-8) is an alternative ligand for PFOA / PFOS detection. Serum albumin serves as the primary binding target for perfluorinated compounds (PFCs) within the human body. Studies revealed that over 90% of perfluorooctanoic acid (PFOA) and perfluoro octane sulfonate (PFOS), respectively, in human blood are bound to serum albumin. Hydrophobic Interactions between Bovine Serum albumin and PFOA: PFOA is a perfluorinated compound with a hydrophobic tail. The hydrophobic regions of PFOA likely interact with hydrophobic pockets or cavities on the surface of serum albumin. Serum albumin has specific binding sites, such as Sudlow's site I and II, located in hydrophobic cavities of its structure. PFOA interacts with these sites in a way that is specific to the chemical structure of PFOA.

[0159] In some embodiments, a combination of Poly (diallyldimethylammonium chloride) (PDAD; CAS Reg. No.26062-79-3) and Decafluorobiphenyl (DFB; CAS Reg. No. 434-90-2) is disclosed herein to detect perfluorooctanoic acid (PFOA) in water through electrochemical output.

[0160] Material properties of PDAD and DFB and their significance are provided herein. PDAD is a positively charged polymer that can electrostatically interact with negatively charged species, such as the carboxylate group of PFOA. This interaction facilitates the binding of PFOA to the sensor surface. Film-forming ability is another material property. PDAD can form a thin, uniform film on the sensor surface, providing a controlled environment for PFOA binding and enhancing the electrochemical response. As a fluorinated compound, DFB is a hydrophobic, fluorinated organic compound that can enhance the binding of PFOA to the sensor. Its structure is compatible with the hydrophobic tail of PFOA, promoting adsorption. Electron-Withdrawing Property is another material property. The presence of fluorine atoms in DFB can affect the electron density on the surface of the sensor, facilitating charge transfer processes during electrochemical measurements.

[0161] Mechanisms of PFOA detection by PDAD and DFB are disclosed herein. The carboxylate group of PFOA (-COO") interacts with the cationic PDAD, leading to the formation of a stable complex. This interaction is crucial for capturing PFOA molecules from the water sample and ensuring their proximity to the sensor surface. DFB's hydrophobic nature allows it to interact favorably with the hydrophobic tail of PFOA. This interaction aids in the effective adsorption of PFOA onto the sensor surface and enhances the overall binding capacity of the sensor. When PFOA binds to the PDAD-DFB layer on the CNT-graphene sensor, the following electrochemical changes occur. Change in Resistance: The adsorption of PFOA alters the chargetransport properties of the sensor. If PFOA binding introduces a barrier to charge flow or occupies conductive pathways, the resistance of the sensor will increase.

[0162] There is a relationship between the output signal and PFOA Concentration. The output signal from the electrochemical sensor (resistance) correlates with the concentration of PFOA in the water sample through the following principles. Higher PFOA Concentration: An increase in PFOA concentration leads to a greater number of binding sites being occupied on the sensor surface, resulting in larger changes in the output signal. This relationship can be linear or nonlinear, depending on the specific interactions and concentration ranges.

[0163] In some embodiments, K2CO3, BCD, and DFB to Detect PFOA in water is disclosed herein. A combination of potassium carbonate (K2CO3; CAS Reg. No. 584-08-7), P-cyclodextrin (BCD; CAS Reg. No. 7585-39-9), and decafluorobiphenyl (DFB; CAS Reg. No. 434-90-2) to detect perfluorooctanoic acid (PFOA) in water via electrochemical output. The buffering effect of K2CO3 and the complex formation capabilities of BCD significantly improve the sensor's ability to capture and detect low concentrations of PFOA. The selective binding of PFOA by BCD, combined with the hydrophobic interactions of DFB, enhances the specificity and stability of the sensor. The favorable binding kinetics of PFOA to the K2CO3-BCD-DFB layer enable quick sensor responses, making the sensor suitable for real-time monitoring.

[0164] DFB-CDP, with its fluorinated crosslinker, was designed to stabilize [3-CD-PFAS inclusion complexes through secondary noncovalent interactions. The fluorinated nature of the DFB crosslinker likely contributes to its high affinity for PFOA compared to other previously synthesized [3-CD polymers.

[0165] As a pH buffering agent, K2CO3 acts as a buffering agent, maintaining a stable pH during the electrochemical measurement. This is crucial since pH can influence the ionization state of PFOA and its interaction with the sensor. K2CO3 can help in promoting ion exchange at the sensor surface, which is beneficial for enhancing the electrochemical response and sensitivity. BCD is a cyclic oligosaccharide that can form inclusion complexes with hydrophobic compounds like PFOA. This encapsulation (molecular encapsulation) improves the solubility and stability of PFOA in aqueous solutions, facilitating its detection. The unique structure of BCD allows for selective binding to PFOA, enhancing the specificity of the sensor. DFB is a fluorinated organic compound that can enhance the binding of PFOA through hydrophobic interactions (hydrophobic nature). Its structure is complementary to the hydrophobic characteristics of PFOA. The presenceof fluorine atoms in DFB can influence the electron density on the sensor surface, which is beneficial for charge transfer during electrochemical measurements.

[0166] Mechanisms of PFOA detection by K2CO3, BCD, and DFB are disclosed herein. Ion Exchange and Stabilization: K2CO3 maintains a stable pH, promoting optimal conditions for the electrochemical reaction. It aids in the ionization of PFOA, enhancing its interaction with the sensor components. Complex Formation with BCD: BCD forms inclusion complexes with PFOA, enhancing its solubility and facilitating its adsorption onto the sensor surface, Where the PFOA molecule is encapsulated within the BCD cavity, enhancing its interaction with the sensor. The hydrophobic tail of PFOA interacts with DFB, promoting additional binding and retention of PFOA on the sensor surface. This complexation allows for improved adsorption of PFOA onto the sensor surface, enhancing the overall sensitivity. Electrochemical Mechanism: When PFOA binds to the K2CO3-BCD-DFB layer on the CNT-graphene sensor, the adsorption of PFOA can alter the charge transport properties of the sensor. The presence of PFOA may increase resistance if it disrupts conductive pathways. The output signal from the electrochemical sensor (resistance) correlates with the concentration of PFOA in the water sample through the following principles:

[0167] In some embodiments, nucleotide aptamers for detecting PFOS is disclosed herein. Aptamers are selected because of their unique selectivity towards various analytes. The aptamer can be immobilized on an electrode surface. Binding of PFOS to the aptamer causes changes in the electrochemical properties at the electrode- solution interface. These changes can be measured using the device disclosed herein. The sulfonate group (-SO3-) of PFOS is negatively charged. Positively charged regions of the aptamer, such as protonated nucleobases (e.g., adenine or cytosine at slightly acidic pH), can interact electrostatically with this negative charge. While PFOS itself doesn't have hydrogen bond donors, its sulfonate group can act as a hydrogen bond acceptor. Nucleobases in the aptamer, particularly guanine and thymine, can form hydrogen bonds with the sulfonate oxygen atoms. The long perfluorinated carbon chain of PFOS is highly hydrophobic. Aptamers can form hydrophobic pockets or regions that interact with this fluorinated chain through van der Waals forces. While not as prominent as with aromatic compounds, there may be some weak 7t-7t interactions between the electron-rich fluorine atoms and the aromatic rings of nucleobases.

[0168] The following 5 aptamers related by ability for secondary structure formation likely to capture PFAS and two anchors as candidates for testing aptamers as capture reagents for PFASwith the use of BNS arrays. (GT)x, x- { 15 to 30}, anchor to the BNS nanocarbons. (CA)y, y= { 15 to 30}, extension anchor to an aptamer.

[0169] (SEQ ID NO: 1) 5 -GGAATCGTGGGTGGTAGGGTAGGGGATGCA -3'

[0034]

[0170] (SEQ ID NO: 2) 5'-GAC TGG GGA TTA GCT AGT AGA TGG TGA TAG TAG GGG TAG AGA TAG TAG CCA TGG TGA TTA GCT AGG GGA T-3’

[0171] (SEQ ID NO: 3) 5'-TGC TCC GTG GGA GGA TGT GTG GGG TGG GTG GGA GGT GTT GTG GGC TTT TTT TTT TTT TTT TTT TTT TTT-3'

[0172] (SEQ ID NO: 4) 5'-CCG CCA ACA AGG AAA TAG AAG GCG CCG GTC GAA AGG TGG TGC AAG AGG TGA AGC AAT GGA AAG GAG GAG AGG GGC GGT TCG TGG TGG ACG-3'

[0173] (SEQ ID NO: 5) 5'-GCA ATC GCA GCC TGG GCA TCT TGG TGT GAA GCG TGA GGG CGA TGA GTA AGT CGG GCA TAG GAG TGG GAA CCT GTT GGG TGA AGG AAA GTT GGA GGC-3'

[0174] In some embodiments, a-Cyclodextrin to detect PFOA in water is disclosed herein, a- Cyclodextrin (a-CD; CAS Reg. No. 10016-20-3) can serve as a strong ligand for detecting perfluorooctanoic acid (PFOA) in water by exploiting both host-guest interactions and hydrophobic interactions due to its unique molecular structure. When a-CD is primed onto a carbon nanotube (CNT) and graphene sensor, it forms a stable, non-covalent interaction with PFOA, enabling effective capture and detection. Here’s an explanation of the binding interactions involved: host-guest inclusion complex, hydrophobic interaction with CNT and graphene surface, weak electrostatic interactions, and changes in electrical properties.

[0175] Host-guest inclusion complex: a-Cyclodextrin is a cyclic oligosaccharide with a toroidal (doughnut-like) structure that has a hydrophobic inner cavity and a hydrophilic outer surface. PFOA is an amphiphilic molecule, with a hydrophilic carboxylate head (-COOH) and a long hydrophobic fluorinated carbon chain. The hydrophobic tail of PFOA is highly compatible with the hydrophobic cavity of a-CD. The hydrophobic tail of PFOA can fit into the cavity of a-CD, forming a host-guest inclusion complex. This complexation occurs due to the entrapment of PFOA’s hydrophobic tail within the hydrophobic core of a-CD, creating a strong, non-covalent binding that stabilizes PFOA on the sensor surface. Hydrophobic Interaction with CNT and Graphene Surface: The hydrophobic nature of both PFOA’s fluorinated tail and the interior of the a-CD cavity enhances the stability of the host-guest complex when it is anchored on the hydrophobic CNT and graphene surface. The CNT and graphene substrate’s hydrophobicity helpsto align and retain PFOA molecules at the sensor surface once they are captured within the a-CD cavity, increasing the sensor’s sensitivity to PFOA. Weak Electrostatic Interactions: Although a- CD itself is not charged, the carboxylate head of PFOA can still experience weak electrostatic attractions with hydroxyl groups on the outer rim of the a-CD molecule. This interaction adds an additional level of stabilization for PFOA within the a-CD cavity. These weak interactions help secure PFOA molecules at the a-CD site on the sensor surface, enhancing binding stability without forming covalent bonds. Changes in Electrical Properties: When PFOA binds to a-CD on the CNT and graphene sensor, it causes slight changes in the sensor’s surface properties, which can alter the electrical conductivity or resistance of the CNT and graphene. These changes are measurable and indicate the presence and concentration of PFOA on the sensor, allowing for sensitive detection.

[0176] In some embodiments, liver fatty acid-binding protein to detect PFOA in water is disclosed herein. Liver fatty acid-binding protein (L-FABP) can serve as a highly effective ligand for detecting perfluorooctanoic acid (PFOA) in water when applied to a carbon nanotube (CNT) and graphene sensor surface. Due to its natural role in binding hydrophobic molecules, particularly long-chain fatty acids, L-FABP is well-suited for capturing and stabilizing PFOA — a perfluorinated compound with a hydrophobic tail and a polar carboxylate head group. Here’s an explanation of the bonding interactions between L-FABP and PFOA on a CNT and graphenebased sensor.

[0177] Hydrophobic Binding Pocket Interaction: L-FABP possesses a hydrophobic binding pocket that can selectively accommodate long-chain hydrophobic molecules, making it ideal for capturing PFOA’s fluorinated carbon chain. PFOA has a highly hydrophobic perfluorinated tail, which can effectively interact with the hydrophobic amino acid residues within the L-FABP binding pocket. This non-covalent interaction helps stabilize PFOA on the sensor surface. Electrostatic Interactions: PFOA has a polar carboxylate head group (-COO ) that can engage in electrostatic interactions with positively charged amino acids in L-FABP’ s binding pocket, such as lysine and arginine residues. These electrostatic attractions further stabilize PFOA’s binding to L-FABP, enhancing the strength of the ligand-target interaction. The presence of this polar interaction allows PFOA to remain bound to L-FABP even when in an aqueous environment, making it a selective and effective capturing mechanism. Hydrogen bonding: L-FABP’ s binding site contains amino acid residues capable of forming hydrogen bonds with the carboxylate group on PFOA, adding an additional level of stability to the PFOA-L-FABP complex. Specifically,hydrogen bonds can form between polar residues on L-FABP, such as serine or threonine, and the oxygen atoms of the carboxylate group in PFOA. These bonds help retain PFOA within the L- FABP binding pocket, especially during detection on the sensor surface. Conformational changes and stabilization: L-FABP can undergo slight conformational changes to accommodate the PFOA molecule more effectively, which further stabilizes the binding complex. These changes allow L- FABP to create a snug fit around the PFOA molecule, enhancing the overall binding strength and stability, and enabling sensitive and selective PFOA detection when applied to the CNT and graphene sensor. Compatibility with CNT and Graphene Sensor Surface: When immobilized on a CNT and graphene sensor, L-FABP’ s ability to bind PFOA is complemented by the conductive properties of the substrate. The interaction between PFOA and L-FABP induces a measurable change in the electrical properties of the sensor, such as changes in resistance or conductivity. The CNT and graphene substrate’s high surface area and conductive nature amplify the sensor’s response to PFOA binding, enhancing the detection sensitivity.

[0178] In some embodiments, Human Hemoglobin to detect PFOA in water is disclosed herein. Human hemoglobin (Hb; CAS Reg. No. 9008-02-0) can function as an effective ligand for detecting perfluorooctanoic acid (PFOA) in water when primed on a carbon nanotube (CNT) and graphene sensor. Hemoglobin has specific structural properties and binding sites that can interact with PFOA, a perfluorinated compound, and facilitate strong binding. Here’s a detailed explanation of the bonding mechanisms and the advantages of using hemoglobin as a ligand for PFOA detection on a CNT and graphene-based sensor, including the role of optimal temperature.

[0179] Hydrophobic interactions with PFOA include Hemoglobin contains hydrophobic pockets and amino acid residues, which can interact favorably with PFOA’s hydrophobic perfluorinated tail. PFOA’s tail consists of a long chain of carbon-fluorine bonds, creating a hydrophobic region that fits well within the hydrophobic pockets of hemoglobin. This interaction serves as the primary binding force between hemoglobin and PFOA, helping to hold PFOA tightly to the ligand. Electrostatic Interactions: PFOA contains a carboxylate group (-COO ) at its head, which introduces a negative charge. Hemoglobin, being a protein, has amino acid residues with positive charges, such as lysine and histidine, which can interact with this negatively charged group on PFOA. The electrostatic attraction between the carboxylate head of PFOA and the positively charged amino groups on hemoglobin helps stabilize the PFOA-hemoglobin complex. This interaction not only strengthens the overall binding but also enhances the selectivity of hemoglobin for PFOA over other potential contaminants. Hydrogen bonding: Hemoglobin contains polargroups that can form hydrogen bonds with the carboxylate group on PFOA. For example, polar amino acids like serine, threonine, and glutamine within hemoglobin can form hydrogen bonds with the oxygen atoms of the PFOA carboxylate. These hydrogen bonds help lock PFOA in place within the hemoglobin binding site, enhancing stability and retention of PFOA on the sensor surface. This provides a stronger and more reliable interaction between the ligand and PFOA, which is advantageous for accurate detection. Conformational flexibility and adaptive binding: Hemoglobin is a globular protein with some degree of structural flexibility, allowing it to adjust slightly to optimize the interaction with PFOA. This conformational adaptability enables hemoglobin to create a snug fit around PFOA, effectively holding it in place on the CNT and graphene sensor surface. This adaptability is beneficial for high-sensitivity applications, as it allows hemoglobin to retain PFOA even under varying environmental conditions. Role of optimal temperature: The binding efficiency between hemoglobin and PFOA can be enhanced by maintaining the CNT and graphene sensor at an optimal temperature. A moderate, controlled temperature (e.g., around 25-37°C) helps maintain hemoglobin’s native structure, allowing it to retain its hydrophobic pockets, positively charged regions, and hydrogen-bonding sites effectively. At optimal temperatures, hemoglobin’s binding sites are more accessible, and its flexibility is maximized, promoting stronger and more stable interactions with PFOA. Excessively low temperatures could reduce flexibility and weaken hydrophobic interactions, while excessively high temperatures might denature hemoglobin, reducing its binding capability. Compatibility with CNT and graphene sensor surface: The CNT and graphene substrate provides a high surface area and excellent conductivity, which enhances the detection capability of the hemoglobin-PFOA complex. The binding of PFOA to hemoglobin induces measurable changes in the sensor’s electrical properties, such as changes in resistance or conductivity. This property amplifies the sensor’s sensitivity, allowing for the rapid and reliable detection of PFOA in water. The strong affinity of hemoglobin for PFOA, coupled with the high sensitivity of the CNT and graphene sensor, makes this setup ideal for detecting trace levels of PFOA in water samples. PFOS / PFOA can act as both ligand and target. Initial Functionalization: Functionalizing or coating the sensor surface (typically a graphene or CNT-based material) with PFOS. The hydrophobic perfluorinated tails of PFOS molecules might self-assemble or adsorb onto the graphene or CNT surface, while the hydrophilic sulfonate head groups may be exposed to the solution. The sulfonic groups can also interact with the sensor's surface, leading to the immobilization of PFOS onto the surface. Target PFOS Detection: When you expose the sensor to water that contains PFOS, the analytePFOS molecules will compete for binding sites with the PFOS molecules already adsorbed on the sensor surface. The target PFOS molecules could bind to the PFOS ligands on the surface through the same type of interactions (71-71 interactions, hydrophobic interactions, or electrostatic interactions). The extent to which the target PFOS binds to the ligand PFOS on the sensor surface could alter the local charge distribution, the surface energy, or the electronic properties of the CNTs or graphene, which can be measured by the sensor. Measuring Changes: When PFOS molecules in the water sample bind to the surface, they will change the electronic, optical, or surface properties of the sensor. For example: Electrochemical Sensing: Binding of PFOS may alter the conductivity of the CNT or graphene. Changes in the resistance can be measured and used to determine the amount of PFOS in the sample.

[0180] In some embodiments, mechanism of using PFOS as both ligand and target is disclosed herein. Molecular Recognition: PFOS molecules are highly specific for interacting with other PFOS molecules due to their unique chemical structure (the hydrophilic sulfonate group and hydrophobic fluorinated tail). This high affinity for each other enables PFOS to act as both a ligand (for initial functionalization of the sensor) and a target (analyte) to be detected. Enhanced Sensitivity: The PFOS molecules, because of their strong binding affinity, create a stable interaction with the sensor. When additional PFOS is added to the system (from the sample), it disrupts or strengthens the binding, which produces a detectable change in the sensor's electrical properties. Self-Assembly: PFOS has a strong tendency to self-assemble on hydrophobic surfaces like graphene or CNTs. This makes it easy to prepare the sensor, as the PFOS molecules naturally orient themselves in a way that maximizes the ligand-target interaction without the need for complex additional functionalization. Real-World Relevance: PFOS is a persistent pollutant, and being able to detect it in environmental samples like water using a sensor functionalized with PFOS is highly valuable. Using PFOS as the ligand directly addresses the contamination and makes the sensor highly specific for PFOS detection in real-world applications.

[0181] In some embodiments, selective detection of short-chain and long-chain PF AS using cyclodextrin is disclosed herein. Cyclodextrin as molecular recognition element: a-Cyclodextrin (a-CD; CAS Reg. No.10016-20-3), consisting of 6 glucose units, has a relatively small hydrophobic cavity (~4.7 A) that is ideal for accommodating small hydrophobic molecules, such as short-chain PFAS with perfluorinated alkyl chains of 4 to 6 carbon atoms (e.g., PFBA, PFBS). The cavity size allows efficient inclusion complex formation with the hydrophobic tails of shortchain PFAS molecules. The hydrophilic sulfonate or carboxylate groups of PFAS interact with theaqueous environment while the hydrophobic perfluorinated chain is encapsulated within the a-CD cavity.

[0182] P-Cyclodextrin (0-CD; No. CAS 7585-39-9), consisting of 7 glucose units, has a larger hydrophobic cavity (~6.0 A) that is well-suited to accommodate long-chain PFAS with perfluorinated alkyl chains of 7 to 12 or more carbon atoms (e.g., PFOA, PFOS). This larger cavity allows for stronger binding interactions with long-chain PFAS molecules, leading to the formation of stable inclusion complexes.

[0183] In some embodiments, sensor functionalization with Cyclodextrins is disclosed herein. The CNT or graphene surface is functionalized with a-cyclodextrin for the selective detection of short-chain PFAS and |3-cyclodextrin for the detection of long-chain PFAS. Functionalization may be achieved through covalent bonding, electrostatic interactions, or jt- it stacking interactions between the cyclodextrin molecules and the carbon-based surface.

[0184] PFAS detection mechanism includes both short-chain PFAS detection and long-chain PFAS detection. Short-Chain PFAS Detection: When short-chain PFAS molecules are introduced into the water sample, they bind to a-cyclodextrin via inclusion complex formation, where the hydrophobic perfluorinated alkyl tail of the PFAS is encapsulated in the a-CD cavity. This binding alters the surface charge, conductivity, of the CNT or graphene platform, which can be detected by electrochemical. The sensor produces a measurable signal proportional to the concentration of short-chain PFAS. Long-Chain PFAS Detection: Similarly, long-chain PFAS molecules in the water sample will interact with P-cyclodextrin, forming inclusion complexes in which the larger hydrophobic perfluorinated alkyl chain of the PFAS molecule is accommodated inside the [3-CD cavity. This results in a detectable change in the sensor's electronic properties, such as a change in charge transfer resistance or current, which can be monitored by electrochemical or optical methods. The sensor’s response is proportional to the concentration of long-chain PFAS.

[0185] Comprehensive PFAS decontamination protocol for sensor components and experimental tools is disclosed herein.

[0186] Cleaning Protocol

[0187] To ensure the integrity of PFAS detection and maintain contamination-free experimental conditions, a comprehensive cleaning protocol has been established for all critical components and tools used in testing, including sensor LIDs and other experimental apparatus. The process begins with cleaning these components in a dishwasher using Alconox Alcojet Low-Foaming Powdered Detergent. Components are stacked appropriately, and three 0.5 tablespoons of detergent are addedbefore initiating Cycle 3 with running tap water. For targeted cleaning, a turbine spinner wash is employed by placing the items in a designated bowl, adding 0.5 tablespoons of detergent, and using the spinner to thoroughly clean the components, with water being replaced between cycles. An ultrasonic cleaner offers enhanced decontamination by immersing the components in a detergent-filled bath set at 50°C with maximum oscillation for 30 minutes, ensuring even microscopic contaminants are addressed. For final rinsing, the components are processed in a dishwasher using distilled water to eliminate any residual impurities.

[0188] Following these cleaning steps, the components are dried on clean paper towels with fans angled downward for 30 minutes, ensuring they remain free of particulates by positioning them hollow-side down where applicable. Finally, the components undergo UV treatment in a specialized cabinet for 30 minutes, ensuring a sterile and contamination-free surface. By adhering to these meticulous cleaning steps, we not only ensure that all tools and components used in experimental testing are free of PFAS contamination but also uphold the reliability and accuracy of our detection systems. All steps are documented in the Cleaning Protocol Checklist, and components are handled exclusively with synthetic polyethylene gloves post-cleaning to prevent recontamination, assuring that our testing processes meet the highest standards of PFAS-free certification.

[0189] The regulation of the dynamic range of detection can be achieved by application of certain coatings to the BNS competing with the BNS and the ligands for capture of the analytes. For example, albumins can be used this way for the detection of the PFOA. The existing laboratory data are for the PFOA in ultrapure water detection with the BNS sensors coated with ovalbumin. The detection dynamic range is shifted in this case from the ppt level illustrated on the BNS modified with copper phthalocyanine blue (figure in the text) to the ppb level. Thus, the presented disclosure pertains to the adaptation of BNS to a variety of detectable analytes and regulation of the dynamic range of the detection as well.

[0190] Signal Processing and Statistical Analysis Method

[0191] This method describes a comprehensive data analysis framework for sensor-based systems, incorporating signal filtering, normalization, advanced statistical computations, and result interpretation. The process uses low-pass filtering to remove noise, normalizes signals for consistent baselines across sensors, and computes key statistical features for quantification. These features include AV (difference between signal points at distinct time intervals), AV normalized by sensor resistance (AV / R), derivatives, slopes, steepness, and the area under the curve (AUC).A cutoff condition is applied to interpret the processed data, providing outputs as categorical results (positive or negative) or precise quantitative measurements, such as concentrations of 100 ppt or 4 ppt, ensuring reliable analysis.

[0192] This invention addresses variability in signal outputs caused by sensor resistance differences and noise interference. It involves the following steps:

[0193] Step 1) Signal Filtering: Raw signals are passed through a low-pass filter to remove high- frequency noise, ensuring smooth signal behavior.

[0194] Step 2) Signal Normalization: Signals are baseline-normalized to ensure uniformity across sensors, reducing discrepancies in baselines.

[0195] Step 3) Delta V and Resistance Normalization: A statistical parameter, AV, is calculated as the difference between two signal points measured at distinct time intervals (tl and t2). AV is normalized by dividing it by the sensor's resistance (AV / R), creating a resistance-independent signal parameter for analysis. This approach compensates for sensor resistance variability, enabling consistent interpretation.

[0196] Step 4) Outlier Detection: Sensors exhibiting anomalous behavior or deviations beyond predefined thresholds are flagged as outliers and excluded from further analysis to maintain accuracy.

[0197] Step 5) Ratio Calculation: Signals from sample-added sensors are compared to a control signal (negative control) by calculating the ratio between the two. This ratio enables relative comparison between the sample and baseline control, enhancing accuracy in identifying significant changes.

[0198] Step 6) Advanced Statistical Features: First and second derivatives are calculated to capture dynamic changes in signal trends. Signal slope and steepness are analyzed to determine transitions and trends in signal patterns. The area under the curve (AUC) is computed to enhance signal quantification and provide a robust comparison metric. Peak Analysis: Compute the difference between the peak or maximum signal value and the initial value to provide additional insight into signal dynamics.

[0199] Step 7) Result Interpretation with Cutoff Conditions: After computing statistical features, a cutoff condition is applied to interpret the processed data, yielding the following outcomes: Positive: Signal meets or exceeds predefined thresholds, indicating the presence of the target parameter. Negative: Signal falls below the threshold, indicating the absence of the target parameter. Quantitative Results: Precise concentration measurements of the target parameter areprovided. For example: signal may indicate 100 ppt (parts per trillion) for higher concentrations.A signal may indicate 4 ppt for trace levels, ensuring accurate quantification.

[0200] This method ensures reliable, consistent outcomes by integrating statistical features like AV / R, sample-to-control ratio, and peak differences with cutoff conditions for a broad range of diagnostic applications.Representative embodiments

[0201] The following embodiments of the present disclosure are provided by way of illustration and example:1. A method for detecting absence or presence of a metal ion or per- and polyfluoroalkyl substances (PFAS) comprising: contacting a bio-nanosensor with a sample, wherein the bio-nanosensor comprises carbon nanotubes, ligands associated with or bound to an outer surface of the carbon nanotubes, and the ligands bound specifically to a target metal ion or PFAS; measuring a change in voltage (AV) resulting from contacting the bio-nanosensor with the sample (test AV); comparing the test AV to a reference AV resulting from contacting a bio-nanosensor with a sample known not to have the metal ion or PFAS; and determining absence or presence of the metal ion or PFAS based on comparing the test AV to the reference AV.2. The method of embodiment 1, wherein the reference AV is determined from contacting a plurality of bio-nanosensors with a plurality of samples known not to have the metal ion or PFAS.3. The method of embodiment 1 or 2, wherein the test AV resulting from binding between the ligands and the target metal ion or PFAS is greater than the reference AV, optionally by a certain absolute amount (e.g., by at least about 0.015 or 0.020 mV, or by at least about 0.02 V) or by a certain relative amount (e.g., by at least about 20%, 30%, 50% or 100%), indicating presence of the metal ion or PFAS.4. The method of embodiment 1 or 2, wherein the test AV resulting from binding between the ligands and the target metal ion or PFAS is less than the reference AV, optionally by a certain absolute amount (e.g., by at least about 0.015 or 0.020 mV, or by at least about 0.02 V) or by a certain relative amount (e.g., by at least about 20%, 30%, 50% or 100%), indicating presence of the metal ion or PFAS.5. The method of embodiment 1 or 2, wherein the test AV is substantially similar to (e.g., within about 10% or 20% of) the reference AV, indicating absence of the metal ion or PFAS.6. The method of any one of the preceding embodiments, which comprises: contacting a plurality of (e.g., about 4, 8, 16, 32, 64 or more) bio-nanosensors with the sample; comparing the test AV generated by each of the bio-nanosensors to the reference AV; and determining presence of the metal ion or PFAS if comparing the test AV to the reference AV for a majority of the bio-nanosensors indicates presence of the metal ion or PFAS, or determining absence of the metal ion or PFAS if comparing the test AV to the reference AV for a majority or half of the bio-nanosensors indicates absence of the metal ion or PFAS.7. The method of any one of embodiments 1 to 6, wherein the ligands are a plurality of a particular ligand.8. The method of any one of embodiments 1 to 7, wherein the ligands are a plurality of two or more different ligand binding specifically to a particular target metal ion or PFAS.9. The method of any one of embodiments 1 to 8, wherein the ligands are a plurality of two or more different ligands binding specifically to two or more different target metal ion or PFAS.10. The method of any one of embodiments 1 to 9, wherein uses a plurality of different ligands binding specifically to a plurality or panel of (e.g., about 2-10, or about 3, 4, 5, 6 or 7) different target metal ion or PFAS to determine absence or presence of a metal ion or a PFAS, whether in the same test or separate tests.11. The method of any one of the preceding embodiments, wherein target metal ion comprise lead, copper and phosphate; wherein PFAS comprises perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA).12. The method of any one of the preceding embodiments, wherein the ligands are selected from (2-Hydroxypropyl)-beta-cyclodextrin (HPpCD; CAS Reg. No. 128446-35-5); Tetramethyl thiuram disulfide (CAS Reg. No. 137-26-8); Disodium bathocuproine Di sulfonate hydrate (CAS Reg. No. 52698-84-7); Tetra thiomolybdate (CAS Reg. No. 15060-55-6); Diethylenetriaminepentaacetic acid (DTPA; CAS Reg. No. 67-43-6); Poly (diallyl dimethylammonium chloride) (PDAD; CAS Reg. No.26062-79-3); Chitosan (CAS Reg. No. 9012-76-4); Ammonium bicarbonate (CAS Reg. No.1066- 33-7); Hexadecyltrimethylammonium bromide (CTAB; CAS Reg. No. 57-09-0); Calcium Hydroxide (CAS Reg. No. 1305-62-0); Nucleotide Aptamers; Copper Phthalocyanine Derivative; Bovine Serum albumin (CAS Reg. No. 9048-46-8); Decafluorobiphenyl (DFB; CAS Reg. No. 434-90-2); potassium carbonate (K2CO3; CAS Reg. No. 584-08-7); decafluorobiphenyl (DFB; CAS Reg. No. 434-90-2);Liver fatty acid-binding protein (L-FABP); Human hemoglobin (Hb; CAS Reg. No. 9008-02-0); Cyclodextrin; a-Cyclodextrin (a-CD; CAS Reg. No.10016-20-3); -Cyclodextrin (0-CD; CAS Reg. No. CAS 7585-39-9); human serum albumin; ammonium hydrogen carbonate; and ovalbumin (albumin from chicken egg).13. The method of embodiment 12, wherein nucleotide aptamers consisting of the nucleotide sequence set forth in SEQ ID NO: 1-5.14. The method of embodiment 12, wherein nucleotide aptamers consisting of the nucleotide sequence at least 80% identical to nucleotide sequence set forth in SEQ ID NO: 1-5.15. The method of any one of the preceding embodiments, wherein the ligands are associated with or bound to the outer surface of the carbon nanotubes by van der Waals force.16. The method of any one of the preceding embodiments, wherein the carbon nanotubes are impregnated with gold nanoparticles and the ligands bound onto the gold nanoparticles.17. The method of any one of the preceding embodiments, wherein the carbon nanotubes are or comprise single-wall carbon nanotubes (SWCNTs).18. The method of embodiment 17, wherein the SWCNTs have a diameter of about 0.5-5 nm and a length of about 2-30 microns.19. The method of any one of embodiments 1 to 18, wherein the carbon nanotubes are or comprise multi-wall carbon nanotubes (MWCNTs).20. The method of embodiment 19, wherein the MWCNTs have: about 2-6 or 6-12 substantially concentric layers of graphene; a diameter of about 5-50 nm or 50-100 nm; and a length of about 2-30 microns.21. The method of any one of the preceding embodiments, wherein the sample is a water sample.22. The method of any one of the preceding embodiments, wherein the sample is an environmental sample.23. The method of embodiment 22, wherein the environmental sample is a water sample from a water-treatment facility (e.g., a sample of wastewater or treated water), an industrial facility (e.g., a sample of wastewater), a business, a domestic residence, a body of water (e.g., a lake, a river or a stream), or a pool of water (e.g., a puddle).24. The method of embodiment 22, wherein the environmental sample is or comprises tap water, groundwater, surface water, drinking water, wastewater, agricultural soils, sediments from rivers, lakes, and estuaries, soil from industrial sites, air samples, biota samples, fish and aquatic organisms,wildlife tissues (e.g., liver, blood), human biological samples (e.g., blood serum, breast milk), food samples, packaged food items, agricultural produce, industrial and household products, treated textiles, firefighting foams, cookware, landfill and leachate samples, solid waste landfill leachates, and soil around landfills.25. The method of any one of the preceding embodiments, which can detect the absence or presence of the metal ion or PFAS with an accuracy of at least about 90%, 95% or 98%.26. The method of any one of the preceding embodiments, wherein the bio-nanosensor(s) is / are prepared with the ligands primed on the outer surface of the carbon nanotubes.27. The method of any one of the preceding embodiments, further comprising contacting the bio- nanosensor(s) with the ligands shortly (e.g., within about 3, 5 or 10 minutes) prior to contacting the bio-nanosensor(s) with the sample.28. The method of embodiment 27, further comprising subtracting the AV resulting from contacting the bio-nanosensor(s) with the ligands from the test AV resulting from contacting the bio- nanosensor(s) with the sample.29. The method of any one of the preceding embodiments, further comprising quantifying the level of the target metal ion or PFAS in the sample based on the test AV.30. The method of embodiment 29, wherein the level of the target metal ion or PFAS in the sample is calculated using an equation (e.g., a substantially linear equation) formulated from changes in voltage resulting from contacting a plurality of bio-nanosensors with a plurality of samples containing known levels of the target metal ion or PFAS.31. The method of embodiment 1 , wherein a target metal ion or PFAS is a metal ion or PFAS, to which a ligand specifically bind.31. A method for processing sensor data, comprising filtering raw signals using a low-pass filter to remove noise; normalizing signal baselines across sensors; calculating change in voltage (AV) as the difference between signal points at distinct intervals and normalizing it by resistance ( V / R); and computing the ratio between the signal from sample-added sensors and the control signal (negative control).32. The method of embodiment 31 , wherein the method further comprising: identifying and excluding outliers based on predefined thresholds; andcomputing advanced statistical features, including first and second derivatives, signal slope, steepness, and area under the curve (AUC).33. The method of embodiment 32, wherein AUC is calculated using the ratio between sample and control signals to quantify cumulative signal differences; a peak difference between the maximum and initial signal values is computed for additional analysis.34. The method of embodiments 31 and 32, wherein processed data is categorized into positive, negative, or quantitative outcomes based on cutoff conditions.35. A system for implementing the method of embodiments 31-34, comprising: a sensor array for data acquisition; a signal processing module for filtering, normalization, and ratio calculations; and a computation engine for deriving advanced statistical features, including AUC and peak analysis, and interpreting results with cutoff conditions.Examples

[0202] The following examples are intended only to illustrate the disclosure. Other procedures, methodologies, techniques, conditions and reagents may alternatively be used as appropriate.Precise PFOS / PFOA Detection Despite Other Interference

[0203] Selective detection of PFOS and PFOA using a single ligand is disclosed in Fig. 3A and Fig. 3B. The different PFAS analytes can be detected and differentiated by adjusting the concentration of the same ligand (HpCD). Comparing the signal strengths of the PFOS and PFOA analytes at the same ligand concentration reveals a significant variation. Taking this into account allows us to verify that the ligand is analyte-specific, as noted by the observed variation in signal intensity (Fig. 3A and Fig. 3B).

[0204] Selective detection of PFOS and PFOA using different ligands is disclosed in Fig 4A and Fig 4B. The device disclosed herein effectively distinguishes between PFOS and PFOA by employing various ligands, each generating distinct signals for these compounds. This differentiation relies on three key factors: 1 ) Distinct Signals for PFOS and PFOA: Each ligand used with the device disclosed herein produces a unique signal when interacting with PFOS or PFOA, allowing clear identification of each compound. 2) Ligand Specificity: Each ligand features specific binding sites and affinities tailored to PFOS or PFOA. These unique molecular interactions enhance the device’s ability to differentiate the two compounds. 3) Signal Variations due to Structural Differences: Structural distinctions between PFOS and PFOA lead to varied interactions with the ligands, resulting in measurable signal variations that further distinguish between them (Fig 4A and Fig 4B).

[0205] The comparative analysis of signal strengths for PFOS and PFOA analytes using different ligands demonstrates a significant variation, which confirms the analyte- specificity of the ligands. The observed differences in signal intensity, coupled with the absence of cross-reactivity, provide strong evidence that each ligand is selectively responsive to its respective target analyte. This variation in response ensures that the sensor can accurately differentiate between PFOS and PFOA, thereby enhancing the precision of the detection system. The specificity of the ligands to individual analytes is a critical feature, ensuring reliable detection without interference from structurally similar compounds. This characteristic is essential for applications requiring precise and selective detection of perfluorinated chemicals, such as environmental monitoring and detection devices.

[0206] The device disclosed herein leverages different ligands to selectively detect PFOS and PFOA, with each ligand producing unique signal strengths depending on its specificity and bindingaffinity. The signal strength for PFOS or PFOA varies by ligand — some ligands yield stronger signals for PFOS, while others favor PFOA — reflecting differences in binding interactions due to each ligand’s unique chemical affinity for the target compound. This ligand- specific approach allows the device disclosed herein to fine-tune detection sensitivity, providing flexible and accurate identification of PFOS and PFOA in complex environmental samples, demonstrating its innovative adaptability in PFAS detection.

[0207] Differentiation of PFOS / PFOA from metals using the same ligand is disclosed herein. The device disclosed herein has been optimized to precisely detect PFOS and PFOA even in the presence of interfering metals such as lead, using the same ligand. Key aspects of this capability include: Higher Signal for PFOS / PFOA: When exposed to PFOS, the sensor generates a significantly higher signal than with metals like lead. This differentiation enables accurate detection of PFOS / PFOA despite the presence of metallic contaminants. Binding Affinity: Due to differences in chemical structure, PFOS / PFOA exhibit higher binding affinities with the ligand than metals, contributing to a stronger and more distinct signal for PFAS compounds. Electrostatic Interactions: PFOS and PFOA have stronger electrostatic interactions with the ligand compared to metals, further enhancing their signal output and making it easier to detect PFOS / PFOA selectively (Fig. 5).

[0208] Sensor preparation for lead, copper, phosphate and PFOS detection includes the following steps: making a PCB with 8 Nano sensors; priming the 8 sensors with the required concentration of ligand such as HPpCD lead and phosphate, PDAD for phosphate and Disodium bathocuproine Di sulfonate hydrate for copper and allow them to dry; and ssembling the PCB with LID.

[0209] Sensor preparation for PFOA detection includes the following two methods. Method 1): Make a PCB with 8 Nano sensors; Prime the 8 sensors with the required concentration of Copper Phthalocyanine or Bovine serum albumin or HpCD and allow them to dry; Assemble the PCB with LID. Method 2): Prepare a saturated solution of Phthalocyanine blue in 91% Isopropyl Alcohol (IPA); Allow excess solids to settle; Aspirate the solution in the desired quantity using a pipette; Mix the solution in a 1:1 ratio with plain BNS paint; Allow excess solvent (IPA) to evaporate naturally or expedite the process with a mild cold airflow; Apply the paint to a 25-micron substrate paper following the protocol; Dry and store the coated substrate in a humidor until needed; Make a PCB with the above 8 Nano sensors; Assemble the PCB with LID.

[0210] Target sample and material include the following. Lead: Millipore Sigma's Lead Nitrate, which is used as a Lead standaid sample for ICP. Phosphate: IX Phosphate-Buffered Saline containing 1.42 g / L Na2HPO4 and 0.24 g / L KH2PO4. Copper: Copper (II) Chloride from Millipore Sigma. PFOS: Perfluoro octane sulfonic acid (PFOS), from Agilent, CA and PFOS sample received from Regional Water Authority, New Haven CT. PFOA: Perfluorooctanoic acid (PFOA) from Millipore sigma. Depending on the desired concentration, dilute the right target with distilled water / tap water / ultra-pure water.

[0211] Test sample preparation includes with following steps: collecting tap water / distilled water / Ultrapure water and spiking with required concentration of target sample (Lead / Phosphate / Copper / PFOS). Test requires a 3mL of final sample volume.

[0212] To conduct PFAS testing, connect the analyzer to a laptop or mobile device via USB or Bluetooth and launch the PFAS Testing App; input the sample details, including the sample ID, type (e.g., water), and the required minimum sample volume of 5 pL. The sample can be introduced by either directly collecting water from the source into a disposable cup or by transferring the sample using a pipette for precise volume control.

[0213] If using the disposable cup method, securely cover the cup with the sensor lid and lock it into the analyzer until it clicks into place. If using a pipette, deposit the sample directly onto the sensor lid following the same locking procedure. Initiate the test using the app, and the analyzer will begin the testing process. The results will be available in approximately 10 to 15 minutes, displayed on the app for review.

[0214] Once the test is complete, remove and discard the disposable cup and sensor lid in accordance with proper waste disposal protocols for PFAS-contaminated materials. This streamlined procedure, whether using the direct sample or pipette method, ensures quick, accurate testing while minimizing contamination risk.

[0215] Testing method of using diclosed metal ions and PFAS detection device (Fig. 6).Water sample collection includes pre-collection preparation and sample collection. The precollection preparation requires hand hygiene and personal protective equipment. Hand hygiene: wash hands thoroughly using soap and water to minimize contamination risk; personal protective equipment (PPE): Wear nitrile gloves throughout the sample collection process to maintain sample purity and avoid cross-contamination. The sample collection includes flushing procedure, container specification and sample preservation. Flushing procedure: prior to sampling, run cold tap water for a minimum of 3 - 5 minutes to flush out any stagnant water from the plumbing,ensuring the sample is representative of the broader water system. Container specification: utilize 250 mL high-density polyethylene (HDPE) containers with polypropylene (PP) caps for sample collection. This combination ensures chemical resistance and prevents potential PFAS contamination from the collection vessel. Sample preservation: after collection, immediately: Direct Testing: Proceed with testing if equipment is available and operational. Sample Storage: If testing is deferred, store the collected sample in a freezer (at 4 - 6°C) to preserve the sample integrity. Ensure the sample is tested within a 28-day window, maintaining the specified temperature range throughout the storage period.

[0216] Water sample testing procedure includes sample preparation, pre-test hygiene, software configuration, sample mixing, testing process and post-test procedure. Sample preparation includes storage condition maintenance: maintain water samples on ice or store in a 4 - 6°C freezer prior to testing to prevent any alteration or degradation of sample integrity. Pre-test hygiene: gloves and hand washing: ensure that hands are washed and nitrile gloves are worn before interacting with the water sample or conducting any testing. Software configuration includes launching the software: open the software on the laptop, confirming that the device reader has been successfully calibrated. Input test parameters: enter the necessary test details into the software, including test name, sensor resistance values, primers, test time (1000 seconds), testing temperature (20°C). Sample mixing includes pre-test mixing: mix the water sample by inverting the bottle a minimum of 10 times to ensure homogeneity. Sample transfer: transfer 3 mL of the mixed sample into the collection cup. Testing process: lid and sensor attachment: secure the device lid with attached sensor to the collection cup containing the water sample. Initiate Testing: on the software interface, select “Start” to initiate the testing process. Follow on-screen instructions for mixing the sample (if required) and ensure the sample reaches the appropriate testing temperature (20°C) before attaching it to the device reader. Post-test procedure: record results: upon test completion, record the test result and ensure the data is stored for analysis. Sample disposal: dispose of the sample in accordance with local environmental regulations for PFAS-contaminated waste.

[0217] Safety and compliance notes are disclosed herein. Personnel training: all personnel involved in water sample collection, device calibration, and sample testing must be adequately trained in the Standard Operating Procedure (SOP) to ensure consistency and accuracy in PFAS detection. Contamination prevention: special care must be taken to avoid contamination during sample collection and testing. Gloves must be worn throughout the procedure, and containers should be handled with minimal direct contact. Equipment calibration frequency: to maintainaccurate results, the device reader must undergo regular calibration as specified by the manufacturer and in accordance with established laboratory standards.

[0218] Data analysis and interpretation are disclosed herein. The first step involves the acquisition or recording of voltage data throughout the specified timeframe, establishing a foundational dataset for subsequent analysis. Subsequently, a data processing phase is implemented, incorporating techniques such as noise elimination, missing values management, and rectification of inconsistencies to enhance the reliability of the acquired data.

[0219] To ensure comparability and consistency, the processed signal is standardized to a predetermined scale, contributing to the accuracy of subsequent analyses. Voltage differentials within a specific time interval (0 to 1000 seconds) are then calculated, and the analysis is further refined using statistical tools to examine the nuances within these voltage differences. The identification of discernible patterns or trends within the voltage differences is a key aspect of the methodology. In this approach, the voltage variance is interpreted concerning a predefined cut-off value using advanced machine learning or deep learning algorithms, introducing an element of automation and efficiency in the analysis process. Finally, both qualitative and quantitative outcomes based on the interpretation are presented, offering a comprehensive understanding of the voltage dynamics. This method provides a robust framework for analyzing voltage data, offering valuable insights for various analytes, as detailed and claimed herein.

[0220] The relationship between the output signal (resistance) and PFOS concentration can often be modeled using calibration curves, there by creatting a quantitative relationship.

[0221] Creating a standard curve by measuring the output signal at various known concentrations of PFOS, a calibration curve can be generated, relating signal strength to concentration. Analyzing signal changes, as PFOS concentration increases, the number of binding sites occupied on the sensor surface increases, leading to a corresponding change in output signal strength.

[0222] Signal strength interpretation is acheieved by higher concentration of PFOS which is indicated by the results in more significant changes in the electrochemical properties of the sensor, leading to a stronger signal (higher current or greater resistance change). Lower Concentration of PFOS, which is indicated by results in smaller changes in the sensor properties, leading to a weaker signal. Sensitivity and Limit of Detection: The sensitivity of the sensor is defined as the change in output signal per unit change in PFOS concentration. A highly sensitive sensor will show a large change in signal for small changes in PFOS concentration, enabling detection at trace levels. Thelimit of detection (LOD) is the lowest concentration of PFOS that can be reliably detected above the background noise of the sensor. This is influenced by the sensor design, material properties, and measurement technique. High-low signal pattern in PFAS detection: The device sensor demonstrates a "High-Low Signal" pattern, where signal strength proportionally decreases with lower PFAS concentrations. This behavior aligns with the Langmuir adsorption isotherm, which indicates that higher PFAS concentrations lead to more binding events, thereby increasing the signal strength. The direct proportional relationship between PFOS concentration and signal output highlights the sensor’s capability for quantitative detection. Additionally, the uniform ligand distribution on the sensor surface ensures consistent binding affinity across various concentrations, enabling reliable and reproducible measurements.

[0223] Lead test data is shown in Fig. 7. (2-Hydroxypropyl)-beta-cyclodextrin [HPpCD] is used as the ligand. Volume and concentration of the ligand on each sensor are 0.0001M and 5pL. Volume of the target Lead sample is ~3mL. Distilled water is the water source. The test data shown in Fig. 7 for each concentration is the mean value from 8 individual sensors. 12-15 MD’s validation test on 15 PPB Lead sample is shown in Fig. 8B. EPA approved instrument data vs data created by the device disclosed herein, in Fig. 8D.

[0224] Phosphate test data is shown in Fig. 8E. Poly (diallyl dimethylammonium chloride) [PDAD] was the ligand. Volume and concentration of the ligand on each sensor are 0.0001M and 5pL. Volume of the target Phosphate sample is ~3mL. The data shown below for each concentration is the mean value from 8 individual sensors. Distilled water is the water source.

[0225] PFOS test data is disclosed Figs. 9A and 9B. PFOS in distilled water: (2-Hydroxypropyl)- beta-cyclodextrin [HPpCD] is the ligand. Volume and concentration of the ligand on each sensor are 0.0001M and 5pL. Volume of the target PFOS sample is ~3mL. The data shown below for each concentration is the mean value from 86 individual sensors. Distilled water is the water source.

[0226] The test data of PFOS in tap water is shown in Fig. 9C. (2-Hydroxypropyl)-beta- cyclodextrin [HPPCD] is the ligand. Volume and concentration of the ligand on each sensor are 0.0001M and 5pL. Volume of the target PFOS sample is ~3mL. The data shown below for each concentration is the mean value from 81 individual sensors. Water source is the tap water from East Haven, CT (Fig. 9C). PFOS in Connecticut Tap and well water is shown in Fig. 9D and EPA 537.1 method LCMSMS result is shown in Fig. 9E.

[0227] Validation of the PFOS detection device based on the consistency with Ic-ms analysis.

[0228] To establish the reliability and accuracy of the device disclosed herein for detecting PFOS in water, conducted extensive testing was conducted on local county tap water samples, including samples from East Haven. Each sample was analyzed using the device disclosed herein, calibrated against a robust standard curve developed to differentiate PFOS presence based on signal intensity relative to a defined action level.

[0229] For additional validation, one of the county water samples was also subjected to Liquid Chromatography-Mass Spectrometry (LC-MS) analysis. The LC-MS results showed that the observed signal was below the detection threshold for PFOS, indicating a "negative" result — meaning no PFOS was detected in the sample. This LC-MS finding directly aligns with the device calibration, where the signal for negative samples falls consistently below the action level.

[0230] These results confirm the capacity to accurately classify PFOS presence or absence in water samples by the device disclosed herein. The consistency between the output of the device disclosed herein and LC-MS data reinforces the device disclosed herein as a reliable, precise tool for PFOS detection, meeting industry standards for environmental monitoring and public health safety.

[0231] PFOS test data is shown in Fig. 10A. CNT and Graphene are the ligands. Volume of the target PFOS sample is ~5pL. The data shown below for each concentration is the mean value from 8 individual sensors. Water Source is East Haven tap water and Ultra-Pure water, one to one ratio (Fig. 10A).

[0232] PFOA test data is shown in Fig. 10B and 10C. Copper Phthalocyanine Derivative [CuPh] is the ligand. Volume and concentration of the ligand on each sensor: The ligand was pre-dissolved in the sensor material. Volume of the target PFOA sample: ~3mL. The data shown below for each concentration is the mean value from 24 individual sensors. Water source is Ultra- Pure Water.

[0233] PFOA Test Data is shown in Fig. 10D.: PDAD + DFB are the ligands. Volume and concentration of the ligand on each sensor is 2uL. Volume of the target PFOA sample is 2uL. The data shown below for each concentration is the mean value from 24 individual sensors. East Haven Tap water and Ultra-Pure water are the water source.

[0234] It is understood that, while particular embodiments have been illustrated and described, various modifications may be made thereto and are contemplated herein. It is also understood that the disclosure is not limited by the specific examples provided herein. The description and illustration of embodiments and examples of the disclosure herein are not intended to be construed in a limiting sense. It is further understood that all aspects of the disclosure are not limited to thespecific depictions, configurations or relative proportions set forth herein, which may depend upon a variety of conditions and variables. Various modifications and variations in form and detail of the embodiments and examples of the disclosure will be apparent to a person skilled in the art. It is therefore contemplated that the disclosure also covers any and all such modifications, variations and equivalents.

Claims

What Is Claimed Is:

1. A method for detecting absence or presence of a metal ion or per- and polyfluoroalkyl substances (PFAS) comprising: contacting a bio-nanosensor with a sample, wherein the bio-nanosensor comprises carbon nanotubes, ligands associated with or bound to an outer surface of the carbon nanotubes, and the ligands bound specifically to a target metal ion or PFAS; measuring a change in voltage (AV) resulting from contacting the bio-nanosensor with the sample (test AV); comparing the test AV to a reference AV resulting from contacting a bio-nanosensor with a sample known not to have the metal ion or PFAS; and determining absence or presence of the metal ion or PFAS based on comparing the test AV to the reference AV.

2. The method of claim 1, wherein the reference AV is determined from contacting a plurality of bio-nanosensors with a plurality of samples known not to have the metal ion or PFAS.

3. The method of claim 1 or 2, wherein the test AV resulting from binding between the ligands and the target metal ion or PFAS is greater than the reference AV, optionally by a certain absolute amount (e.g., by at least about 0.015 or 0.020 mV, or by at least about 0.02 V) or by a certain relative amount (e.g., by at least about 20%, 30%, 50% or 100%), indicating presence of the metal ion or PFAS.

4. The method of claim 1 or 2, wherein the test AV resulting from binding between the ligands and the target metal ion or PFAS is less than the reference AV, optionally by a certain absolute amount (e.g., by at least about 0.015 or 0.020 mV, or by at least about 0.02 V) or by a certain relative amount (e.g., by at least about 20%, 30%, 50% or 100%), indicating presence of the metal ion or PFAS.

5. The method of claim 1 or 2, wherein the test AV is substantially similar to (e.g., within about 10% or 20% of) the reference AV, indicating absence of the metal ion or PFAS.

6. The method of any one of the preceding claims, which comprises: contacting a plurality of (e.g., about 4, 8, 16, 32, 64 or more) bio-nanosensors with the sample; comparing the test AV generated by each of the bio-nanosensors to the reference AV; and determining presence of the metal ion or PFAS if comparing the test AV to the reference AV for a majority of the bio-nanosensors indicates presence of the metal ion or PFAS, or determining absence of the metal ion or PFAS if comparing the test AV to the reference AV for a majority or half of the bio-nanosensors indicates absence of the metal ion or PFAS.

7. The method of any one of claims 1 to 6, wherein the ligands are a plurality of a particular ligand.

8. The method of any one of claims 1 to 7, wherein the ligands are a plurality of two or more different ligand binding specifically to a particular target metal ion or PFAS.

9. The method of any one of claims 1 to 8, wherein the ligands are a plurality of two or more different ligands binding specifically to two or more different target metal ion or PFAS.

10. The method of any one of claims 1 to 9, wherein uses a plurality of different ligands binding specifically to a plurality or panel of (e.g., about 2-10, or about 3, 4, 5, 6 or 7) different target metal ion or PFAS to determine absence or presence of a metal ion or a PFAS, whether in the same test or separate tests.

11. The method of any one of the preceding claims, wherein target metal ion comprises lead, copper or phosphate; wherein PFAS comprises perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA).

12. The method of any one of the preceding claims, wherein the ligands are selected from (2- Hydroxypropyl)-beta-cyclodextrin (HP0CD; CAS Reg. No. 128446-35-5); Tetramethyl thiuram disulfide (CAS Reg. No. 137-26-8); Disodium bathocuproine Di sulfonate hydrate (CAS Reg. No. 52698-84-7); Tetra thiomolybdate (CAS Reg. No. 15060-55-6); Diethylenetriaminepentaacetic acid (DTPA; CAS Reg. No. 67-43-6); Poly (diallyl dimethylammonium chloride) (PDAD; CAS Reg. No.26062-79-3); Chitosan (CAS Reg. No. 9012-76-4); Ammonium bicarbonate (CAS Reg. No.1066- 33-7); Hexadecyltrimethylammonium bromide (CTAB; CAS Reg. No. 57-09-0); Calcium Hydroxide (CAS Reg. No. 1305-62-0); Nucleotide Aptamers; Copper Phthalocyanine Derivative; Bovine Serum albumin (CAS Reg. No. 9048-46-8); Decafluorobiphenyl (DFB; CAS Reg. No. 434-90-2); potassium carbonate (K2CO3; CAS Reg. No. 584-08-7); decafluorobiphenyl (DFB; CAS Reg. No. 434-90-2); Liver fatty acid-binding protein (L-FABP); Human hemoglobin (Hb; CAS Reg. No. 9008-02-0); Cyclodextrin; a-Cyclodextrin (a-CD; CAS Reg. No.10016-20-3); P-Cyclodextrin ([LCD; CAS Reg. No. CAS 7585-39-9); human serum albumin; ammonium hydrogen carbonate; and ovalbumin (albumin from chicken egg).

13. The method of claim 12, wherein nucleotide aptamers consisting of the nucleotide sequence set forth in SEQ ID NO: 1-5.

14. The method of claim 12, wherein nucleotide aptamers consisting of the nucleotide sequence at least 80% identical to nucleotide sequence set forth in SEQ ID NO: 1-5.

15. The method of any one of the preceding claims, wherein the ligands are associated with or bound to the outer surface of the carbon nanotubes by van der Waals force.

16. The method of any one of the preceding claims, wherein the carbon nanotubes are impregnated with gold nanoparticles and the ligands bound onto the gold nanoparticles.

17. The method of any one of the preceding claims, wherein the carbon nanotubes are or comprise single- wall carbon nanotubes (SWCNTs).

18. The method of claim 17, wherein the SWCNTs have a diameter of about 0.5-5 nm and a length of about 2-30 microns.

19. The method of any one of claims 1 to 18, wherein the carbon nanotubes are or comprise multiwall carbon nanotubes (MWCNTs).

20. The method of claim 19, wherein the MWCNTs have: about 2-6 or 6-12 substantially concentric layers of graphene; a diameter of about 5-50 nm or 50-100 nm; and a length of about 2-30 microns.

21. The method of any one of the preceding claims, wherein the sample is a water sample.

22. The method of any one of the preceding claims, wherein the sample is an environmental sample.

23. The method of claim 22, wherein the environmental sample is a water sample from a watertreatment facility (e.g., a sample of wastewater or treated water), an industrial facility (e.g., a sample of wastewater), a business, a domestic residence, a body of water (e.g., a lake, a river or a stream), or a pool of water (e.g., a puddle).

24. The method of claim 22, wherein the environmental sample is or comprises tap water, groundwater, surface water, drinking water, wastewater, agricultural soils, sediments from rivers, lakes, and estuaries, soil from industrial sites, air samples, biota samples, fish and aquatic organisms, wildlife tissues (e.g., liver, blood), human biological samples (e.g., blood serum, breast milk), food samples, packaged food items, agricultural produce, industrial and household products, treated textiles, firefighting foams, cookware, landfill and leachate samples, solid waste landfill leachates, and soil around landfills.

25. The method of any one of the preceding claims, which can detect the absence or presence of the metal ion or PF AS with an accuracy of at least about 90%, 95% or 98%.

26. The method of any one of the preceding claims, wherein the bio-nanosensor(s) is / are prepared with the ligands primed on the outer surface of the carbon nanotubes.

27. The method of any one of the preceding claims, further comprising contacting the bio- nanosensor(s) with the ligands shortly (e.g., within about 3, 5 or 10 minutes) prior to contacting the bio-nanosensor(s) with the sample.

28. The method of claim 27, further comprising subtracting the AV resulting from contacting the bio-nanosensor(s) with the ligands from the test AV resulting from contacting the bio-nanosensor(s) with the sample.

29. The method of any one of the preceding claims, further comprising quantifying the level of the target metal ion or PFAS in the sample based on the test AV.

30. The method of claim 29, wherein the level of the target metal ion or PFAS in the sample is calculated using an equation (e.g., a substantially linear equation) formulated from changes in voltage resulting from contacting a plurality of bio-nanosensors with a plurality of samples containing known levels of the target metal ion or PFAS.

31. A method for processing sensor data, comprising filtering raw signals using a low-pass filter to remove noise; normalizing signal baselines across sensors; calculating change in voltage (AV) as the difference between signal points at distinct intervals and normalizing it by resistance (AV / R); and computing the ratio between the signal from sample-added sensors and the control signal (negative control).

32. The method of claim 31, wherein the method further comprising: identifying and excluding outliers based on predefined thresholds; and computing advanced statistical features, including first and second derivatives, signal slope, steepness, and area under the curve (AUC).

33. The method of claim 32, wherein AUC is calculated using the ratio between sample and control signals to quantify cumulative signal differences; a peak difference between the maximum and initial signal values is computed for additional analysis.

34. The method of claims 31 and 32, wherein processed data is categorized into positive, negative, or quantitative outcomes based on cutoff conditions.

35. A system for implementing the method of claims 31-34, comprising: a sensor array for data acquisition; a signal processing module for filtering, normalization, and ratio calculations; anda computation engine for deriving advanced statistical features, including AUC and peak analysis, and interpreting results with cutoff conditions.

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