Method and apparatus for determining the nanoparticle properties of nanoparticles in a sample

Abstract

A method for determining nanoparticle characteristics of nanoparticles 2 contained in a sample 1, comprising the steps of collecting successive frames of images by utilizing an interference microscope arrangement 110, where the sample 1 is illuminated with illumination light 3 from a coherent light source arrangement 111 and the images are generated by scattered light 4 from the nanoparticles 2 superimposed with a non-scattered reference light, the scattered light and the reference light having a wavelength greater than a cross-sectional dimension of the nanoparticles 2, and tracking the nanoparticles 2 in the successive frames of images, where at least one interference point spread function (iPSF) characteristic of each nanoparticle 2 is established, including the nanoparticle position within each frame. The method includes the steps of: determining nanoparticle trajectory motion data for each nanoparticle 2; calculating for the nanoparticle 2 a nanoparticle size d from the nanoparticle trajectory motion data and an interferometric nanoparticle contrast from at least one iPSF feature of the nanoparticle; generating a two-parametric nanoparticle scatter plot, where each nanoparticle 2 has a plot position based on its calculated nanoparticle size d and calculated interferometric nanoparticle contrast C, and all nanoparticles 2 generate a distribution of nanoparticle plot positions; and analyzing the distribution of nanoparticle plot positions to provide nanoparticle characteristics. Further, a test apparatus 100 for performing the method is described.

Description

[Technical Field]

[0001] The present invention relates to a method and testing apparatus for determining the nanoparticle properties of nanoparticles contained in a sample, for example, for studying bio-nanoparticles in liquids such as aqueous solutions, such as macromolecules. The applications of the present invention are available in the fields of physical, chemical, and / or biological sample investigation. [Background technology]

[0002] This specification references the following prior art, which exemplifies the technical background of the present invention, particularly the technical background of the optical detection of nanoparticles in liquid samples. [1] Zhu,X.,Shen,J.&Song,L.Accurate retrieval of bimodal particle size distribution in dynamic light scattering.IEEE Photonic.Tech.L.28,311-314(2016); [2] Malloy, A. & Carr, B. NanoParticle tracking analysis-the halo TM system.Part.Part.Syst.Char.23,197-204(2006); [3] Filipe, V., Hawe, A. & Jiskoot, W. Critical evaluation of nanoparticle tracking analysis (NTA) by NanoSight for the measurement of nanoparticles and protein aggregates.Pharm.Res.27,796-810(2010); [4] Maguire,C.M.,Roesslein,M.,Wick,P.&Prina-Mello,A.Characterisation of particles in solution a perspective on light scattering and comparative technologies.Sci.Technol.Adv.Mat.19,732-745(2018); [5] Maguire,C.M.et al.Benchmark of nanoparticle tracking analysis on measuring nanoparticle sizing and concentration.J.Micro Nano-Manuf.5(2017); [6] Lee,S.-H.et al.Characterizing and tracking single colloidal particles with video holographic microscopy.Opt.Express 15,18275(2007); [7] Verpillat,F.,Joud,F.,Desbiolles,P.&Gross,M.Darkfield digital holographic microscopy for 3D-tracking of gold nanoparticles.Opt.Express 19,26044(2011); [8] Midtvedt,D.,et al.Size and Refractive Index Determination of Subwavelength Particles and Air Bubbles by Holographic Nanoparticle Tracking Analysis.Anal.Chem.92,1908-1915(2020); [9] Midtvedt,B.et al.Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography.ACS Nano(2021);

[10] Lindfors,K.,Kalkbrenner,T.,Stoller,P.&Sandoghdar,V.Detection and spectroscopy of gold nanoparticles using supercontinuum white light confocal microscopy.Phys.Rev.Lett.93,037401-1(2004);

[11] Taylor,R.W.&Sandoghdar,V.Interferometric Scattering Microscopy:Seeing Single Nanoparticles and Molecules via Rayleigh Scattering.Nano Lett.19,4827-4835(2019);

[12] Piliarik,M.&Sandoghdar,V.Direct optical sensing of single unlabelled proteins and super-resolution imaging of their binding sites.Nat.Commun.5,1-8(2014);

[13] Young,G.et al.Quantitative mass imaging of single biological macromolecules.Science 360(2018);

[14] Taylor,R.et al.Interferometric scattering microscopy reveals microsecond nanoscopic protein motion on a live cell membrane.Nature Photonics,vol.13,July 2019,480-487;

[15] Richard W. Taylor and Vahid Sandoghdar. Interferometric Scattering (iSCAT) Microscopy and Related Techniques. Chapter 2 of 「Label-Free Super-Resolution Microscopy」, Springer, 2019, pp. 25 - 65;

[16] Michalet, X. Mean square displacement analysis of single-particle trajectories with localization error: Brownian motion in an isotropic medium. Phys. Rev. E82, 1 - 13 (2010);

[17] Mahmoodabadi, R. G. et al. Point spread function in interferometric scattering microscopy (iSCAT). Part I: aberrations in defocusing and axial localization. Opt. Express 28, 25969 - 25988 (2020);

[18] Kashkanova, A. D. et al. Precision single-particle localization using radial variance transform. Opt. Express 29, 11070 - 11083 (2021);

[19] Bohren, C. & Huffman, D. Absorption and Scattering of Light by Small Particles (John Wiley & Sons, Ltd, 1998);

[20] Lyklema, J., Rovillard, S. & Coninck, J. D. E. Electrokinetics: the properties of the stagnant layer unraveled. Langmuir 442 14, 5659 - 5663 (1998);

[21] Altman,D.G.&Bland,J.M.Standard deviations and standard errors.BMJ 331,903(2005);

[22] Pedregosa,F.et al.Scikit-learn:Machine Learning in Python.J.Mach.Learn.Res.12,2825-2830(2011);

[23] Bai,K.,Barnett,G.V.,Kar,S.R.&Das,T.K.Interference from proteins and surfactants on particle size distributions measured by nanoparticle tracking analysis(NTA).Pharmaceutical Research 34,800-808(2017);

[24] Hartjes,T.,Mytnyk,S.,Jenster,G.,van Steijn,V.&van Royen,M.Extracellular vesicle quantification and characterization:Common methods and emerging approaches.Bioengineering 6,7(2019);

[25] Pe’rez-Cabezas,B.et al.More than just exosomes:distinct Leishmania infantum extracellular products potentiate the establishment of infection.J.Extracell.Vesicles 8,1541708-1541708(2018);

[26] Van Der Pol, E., Coumans, F. A., Sturk, A., Nieuwland, R. & Van Leeuwen, T. G. Refractive index determination of nanoparticles in suspension using nanoparticle tracking analysis. Nano Lett. 14, 6195 - 6201 (2014); and

[27] Gardiner, C. et al. Measurement of refractive index by nanoparticle tracking analysis reveals heterogeneity in extracellular vesicles. J. Extracell. Vesicles 3, 1 - 6 (2014).

[0003] In a wide range of fields such as medicine, pharmacy, food industry, cosmetics, agriculture, and atmospheric science and environmental research, it is widely known that size and refractive index constitute two important quantities of nanoparticles. Usually, nanoparticles are provided as a monodisperse distribution having a single particle size distribution and / or as a polydisperse distribution having a plurality of particle size distributions. Various techniques such as electron microscopy (EM) or optical methods can be utilized to determine the particle size distribution. Although EM provides excellent resolution in direct imaging, it has substantial limitations regarding sample preparation, low speed, and ex situ measurement characteristics. In fact, optical methods are dominant in nanoparticle measurement techniques because they are fast and can be applied to a wide set of samples in the liquid phase despite the inherent diffraction limit.

[0004] As an example of an optical method for investigating nanoparticles, dynamic light scattering (DLS) [1] utilizes the temporal correlation of light scattered or emitted by an aggregate of diffusive particles. In this method, particle size is extracted from a statistical analysis of the autocorrelation function of light intensity. Today, DLS is the most commonly used technique in particle sizing because it is easy to use and provides high accuracy for mean values. However, this method has low size resolution, which presents limitations in the analysis of polydisperse solutions.

[0005] A more recent approach called nanoparticle tracking analysis (NTA) quantifies the diffusion constant by recording and analyzing the trajectory of a single particle.[2][3] NTA can utilize a variety of imaging methods and does not require extensive calibration, however, a wealth of knowledge about technical parameters such as camera pixel size and frame rate, as well as the temperature and viscosity of the medium, is required to improve the measurement results.

[0006] The oldest imaging method for detecting non-luminescent nanoparticles is dark-field imaging (DFM) [4]. The DFM signal is the scattering cross-section σ of the particle. sca It is proportional to, and as a result, d 6 The particle is weighed as d, where d represents the cross-sectional dimension of the particle, as well as the diameter when considering spherical nanoparticles for simplification. Currently, one of the leading NTA instruments (NanoSight, Malvern Instruments) uses DFM and is considered effective for small gold nanoparticles (GNP) of about 30 nm and polystyrene (PS) particles larger than 60 nm ([3], [5]). Holography is also used for imaging and tracking particles, but the reported sensitivity corresponds to the scattering cross-section of PS particles with a relatively large diameter d of about 300 nm ([6]~[9]). Despite the application of advanced algorithms, the detection and identification of particles in polydisperse solutions remains challenging, particularly in the sub-30 nm region of GNP and the sub-100 nm region of low refractive index particles.

[0007] Therefore, more than 30 years after the advent of nanotechnology, characterizing nanoparticles remains a challenge in many applications, particularly when high processing volume and non-invasiveness are desired.

[0008] Another known optical signal used to study nanoparticles is interference detection of scattering (iSCAT) (

[10] -

[11] ), which has demonstrated ultra-high sensitivity to nanoparticles down to single proteins (

[12] ,

[13] ). Conventionally, iSCAT microscopy has had a limited scope of application, particularly covering the detection of single particles or monitoring only particle motion (

[14] ,

[15] ). [Prior art documents] [Non-patent literature]

[0009] [Non-Patent Document 1] Zhu,X.,Shen,J.&Song,L.Accurate retrieval of bimodal particle size distribution in dynamic light scattering.IEEE Photonic.Tech.L.28,311-314(2016) [Non-Patent Document 2] Malloy,A.&Carr,B.NanoParticle tracking analysis-the haloTM system.Part.Part.Syst.Char.23,197-204(2006) [Non-Patent Document 3] Filipe, V., Hawe, A. & Jiskoot, W. Critical evaluation of nanoparticle tracking analysis (NTA) by NanoSight for the measurement of nanoparticles and protein aggregates.Pharm.Res.27,796-810(2010) [Non-Patent Document 4] Maguire,CM,Roesslein,M.,Wick,P.&Prina-Mello,A.Characterization of particles in solution a perspective on light scattering and comparative technologies.Sci.Technol.Adv.Mat.19,732-745(2018) [Non-Patent Document 5] Maguire,CMet al.Benchmark of nanoparticle tracking analysis on measuring nanoparticle sizing and concentration.J.Micro Nano-Manuf.5(2017) [Non-Patent Document 6] Lee,S.-H.et al.Characterizing and tracking single colloidal particles with video holographic microscopy.Opt.Express 15,18275(2007) [Non-Patent Document 7] Verpillat,F.,Joud,F.,Desbiolles,P.&Gross,M.Darkfield digital holograhic microscopy for 3D-tracking of gold nanoparticles.Opt.Express 19,26044(2011) [Non-Patent Document 8] Midtvedt,D.,et al.Size and Refractive Index Determination of Subwavelength Particles and Air Bubbles by Holographic Nanoparticle Tracking Analysis.Anal.Chem.92,1908-1915(2020) [Non-Patent Document 9] Midtvedt,B.et al.Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography.ACS Nano(2021) [Non-Patent Document 10] Lindfors, K., Kalkbrenner, T., Stoller, P. & Sandoghdar, V. Detection and spectroscopy of gold nanoparticles using supercontinuum white light confocal microscopy.Phys.Rev.Lett.93,037401-1(2004) [Non-Patent Document 11] Taylor, RW & Sandoghdar, V. Interferometric Scattering Microscopy:Seeing Single Nanoparticles and Molecules via Rayleigh Scattering.Nano Lett.19, 4827-4835(2019) [Non-Patent Document 12] Piliarik, M. & Sandoghdar, V. Direct optical sensing of single unlabelled proteins and super-resulution imaging of their binding sites.Nat.Commun.5,1-8(2014) [Non-Patent Document 13] Young,G.et al.Quantitative mass imaging of single biological macromolecules.Science 360(2018) [Non-Patent Document 14] Taylor, R. et al.Interferometric scattering microscopy reveals microsecond nanoscopic protein motion on a live cell membrane.Nature Photonics,vol.13,July 2019,480-487 [Non-Patent Document 15] Richard W.Taylor and Vahid Sandoghdar.Interferometric Scattering(iSCAT)Microscopy and Related Techniques.“Label-Free Super-Resolution Microscopy”,Springer,2019,Chapter 2,pp25-65 [Non-Patent Document 16] Michalet,X.Mean square displacement analysis of single-particle trajectories with localization error:Brownian motion in an isotropic medium.Phys.Rev.E82,1-13(2010) [Non-Patent Document 17] Mahmoodabadi,RGet al.Point spread function in interferometric scattering microscopy(iSCAT).Part I:aberrations in defocusing and axial localization.Opt.Express 28,25969-25988(2020) [Non-Patent Document 18] Kashkanova,ADet al.Precision single-particle localization using radial variance transform.Opt.Express 29,11070-11083(2021) [Non-Patent Document 19] Bohren, C. & Huffman, D. Absorption and Scattering of Light by Small Particles (John Wiley & Sons, Ltd, 1998) [Non-Patent Document 20] Lyklema, J., Rovillard, S. & Coninck, JD Electrokinetics: the properties of the stagnant layer unraveled. Langmuir 442 14, 5659-5663 (1998) [Non-Patent Document 21] Altman,DG&Bland,JMStandard deviations and standard errors.BMJ 331,903(2005) [Non-Patent Document 22] Pedregosa,F.et al.Scikit-learn:Machine Learning in Python.J.Mach.Learn.Res.12,2825-2830(2011) [Non-Patent Document 23] Bai, K., Barnett, GV, Kar, SR&Das, TKInterference from proteins and surfactants on particle size distributions measured by nanoparticle tracking analysis(NTA).Pharmaceutical Research 34,800-808(2017) [Non-Patent Document 24] Hartjes, T., Mytnyk, S., Jenster, G., van Steijn, V. & van Royen, M. Extracellular vesicle quantification and characterization: Common methods and emerging approaches.Bioengineering 6,7(2019) [Non-Patent Document 25] Pe'rez-Cabezas,B.et al.More than just exosomes:distinct Leishmania infantum extracellular products potentiate the establishment of infection.J.Extracell.Vescicles 8,1541708-1541708(2018) [Non-Patent Document 26] Van Der Pol, E., Coumans, FA, Sturk, A., Nieuwland, R. & Van Leeuwen, TG Refractive index determination of nanoparticles in suspension using nanoparticle tracking analysis. Nano Lett. 14, 6195-6201 (2014) [Non-Patent Document 27] Gardiner,C.et al.Measurement of refractive index by nanoparticle tracking analysis reveals heterogeneity in extracellular vesicles.J.Extracell.Vesicles 3,1-6(2014) [Overview of the project] [Problems that the invention aims to solve]

[0010] The object of the present invention is to provide an improved method for determining the nanoparticle properties of nanoparticles contained in a sample, which avoids the drawbacks of conventional methods. In particular, the method is intended to enable the determination of nanoparticle properties with improved size resolution, such as the EM method, while maintaining the advantages of optical methods with respect to sample preparation and in-situ measurement. Furthermore, the method is intended to be able to convey a wide range of information about nanoparticles, such as nanoparticle size, scattering cross-section, and refractive index. The method is intended to facilitate the investigation of multiple nanoparticles in monodisperse or polydisperse solutions and / or enable the determination of the properties of nanoparticles having a size below the diffraction limit, for example, less than 100 nm. In a further embodiment, the nanoparticle properties are intended to be determined with higher accuracy and / or speed and / or with easy implementation of the measurement setup. Furthermore, the object of the present invention is to provide a reasonably improved test apparatus for determining the nanoparticle properties of nanoparticles contained in a sample, which avoids the drawbacks of conventional methods. [Means for solving the problem]

[0011] The above objectives are achieved by a method and / or test apparatus for determining the nanoparticle properties of nanoparticles contained in a sample, comprising features of the independent claim. Advantageous embodiments and applications of the present invention are set forth in the dependent claims.

[0012] According to a first general aspect of the present invention, the above object is resolved by a method for determining the nanoparticle properties of nanoparticles contained in a sample, comprising the steps of: collecting a series of frames of images by utilizing an interference microscope, wherein the sample is illuminated with illumination light from a coherent light source, and the image is generated by scattered light from nanoparticles superimposed with a non-scattered reference light, wherein the scattered light and the reference light have wavelengths greater than the cross-sectional dimensions of the particles.

[0013] Furthermore, a method for determining nanoparticle characteristics includes the steps of tracking nanoparticles in a sequence of images, establishing at least one interference point spreading function (iPSF) feature for each nanoparticle, and determining nanoparticle trajectory motion data for each nanoparticle, including the nanoparticle position in each frame. In particular, the trajectory motion data includes the nanoparticle position and acquisition time for all nanoparticles in the relevant frames. For each nanoparticle, the nanoparticle size (a quantity representing the nanoparticle size) is calculated from the nanoparticle trajectory motion data, and the interference nanoparticle contrast is calculated from at least one iPSF feature of the nanoparticle. Advantageously, the nanoparticle trajectory data can be determined from the center of the iPSF when the nanoparticle moves within or outside the focal plane of the microscope.

[0014] Furthermore, a method for determining nanoparticle properties includes the steps of creating a two-parametric nanoparticle scatter plot, where each nanoparticle has a plot position based on a calculated nanoparticle size and a calculated interference nanoparticle contrast, and all nanoparticles generate a distribution of nanoparticle plot positions, and analyzing the distribution of nanoparticle plot positions to provide nanoparticle properties. Analyzing the distribution of nanoparticle plot positions preferably includes estimating nanoparticle properties using a model that includes nanoparticle properties as parameters, such as generalized Mie theory, from directly from the nanoparticle plot positions, for example from the generation of separable subdistributions (cloud of positions), and / or from a histogram of nanoparticle plot positions, and / or from numerical simulations of nanoparticle plot positions or data derived from numerical simulations, such as isosurfaces of a constant nanoparticle size and nanoparticle scattering cross-section.

[0015] According to a second general aspect of the present invention, the above object is solved by a test apparatus configured for determining the nanoparticle properties of nanoparticles contained in a sample, the test apparatus comprising an interference microscope, a recording device, and an analysis device. The interference microscope comprises a coherent light source, an imaging optical system, a sample container, and a detector camera device, wherein the coherent light source is positioned to illuminate the sample in the sample container with illumination light, and the detector camera device is positioned to collect a series of frames of images generated by scattered light from nanoparticles superimposed with a non-scattered reference light, the scattered light and the reference light having wavelengths larger than the cross-sectional dimensions of the particles.

[0016] The analysis apparatus is positioned to establish at least one interference point spreading function (iPSF) feature of the nanoparticles, to track the nanoparticles within a sequence of images, and to determine nanoparticle trajectory motion data for each nanoparticle, including the position of the nanoparticle within each frame.

[0017] The analysis apparatus is further configured to calculate the nanoparticle size from the trajectory motion data of each nanoparticle, to calculate the nanoparticle scattering cross section from at least one iPSF feature of each nanoparticle, to create a two-parametric nanoparticle scatter plot in which each nanoparticle has a plot position determined by its calculated nanoparticle size and calculated interference nanoparticle contrast, and all nanoparticles generate a distribution of nanoparticle plot positions, and to analyze the distribution of nanoparticle plot positions for the purpose of providing nanoparticle properties. Preferably, the test apparatus or embodiment thereof is configured to carry out a method according to a first general aspect of the present invention or an embodiment thereof.

[0018] The term “iPSF features” typically refers to a specific quantity of an iPSF, preferably at least one of the following: iPSF contrast, particularly the height of the central lobe of the iPSF; integrated iPSF, particularly the overall brightness of the iPSF; and iPSF shape, particularly the shape features of the central and side lobes of the iPSF. iPSF features preferably enable the calculation of interference nanoparticle contrast, such as interference scattering (iSCAT) contrast. Alternatively, another interference nanoparticle contrast, such as the contrast obtained by interference holography, may be calculated.

[0019] The term "interference nanoparticle contrast" typically refers to the amount by which the measured iPSF signal is greater than the background level of the measurement, i.e., the background level of frame acquisition using an interference microscope. In particular, "interference nanoparticle contrast" may be a quantity determined by the scattering cross-section of the nanoparticles. Examples of contrast include the maximum positive contrast, which indicates the height at which the central lobe of a bright iPSF peak protrudes above the background level, or the maximum negative contrast, which indicates the lowness at which the central lobe of a dark peak drops below the background level, or the root mean square (RMS) contrast. Particularly preferably, the maximum interference nanoparticle contrast is calculated so as to capture a moment in which the particles are advantageously within the focal plane of illumination and the effects of frame-to-frame contrast changes due to particle movement out of the focal plane can be avoided.

[0020] The sample is a predetermined amount of liquid, such as water, an aqueous solution, an organic liquid, or a mixture thereof, containing dispersed nanoparticles of the study subject. As a result of thermally induced collisions between the nanoparticles and surrounding liquid molecules, the nanoparticles move through the liquid, for example, due to Brownian motion. Typically, the motion of the nanoparticles is diffusion within the liquid and is optionally superimposed with other forces within the liquid, such as electric and / or magnetic and / or optical forces.

[0021] To collect a series of frames of images of a sample containing nanoparticles, the sample is placed in a volume that can be imaged using an interference microscope, for example, on a substrate, or in a free droplet placed in a sample container such as a chamber and / or a compartmentalized structure of a fluid microsystem separated by transparent walls. The image is generated by collecting the superposition of scattered light from each nanoparticle (illumination light scattered by the nanoparticles) and unscattered reference light, for example, light from a reference light source or light reflected by a surface that compartmentalizes the liquid. Thus, the image can be understood as an interference pattern generated by the imaged sample, particularly by the nanoparticles in the sample.

[0022] The inventors have found that the combination of the NTA measurement concept and, in particular, interference scattering (iSCAT) microscopy offers substantial advantages in examining multiple nanoparticles in liquid samples. Although iSCAT microscopy has already been used to track tasks (

[14] ,

[15] ), these previous measurements were limited to monitoring and characterizing particle motion only. In contrast to conventional methods, the present invention uses nanoparticle trajectory motion data to calculate the nanoparticle size of each nanoparticle.

[0023] Generally, nanoparticles include particles with specific cross-sectional dimensions, such as diameter, in the range of 5 nm to 500 nm, particularly in the range of 5 nm to 150 nm. For spherical or nearly spherical nanoparticles, the specific cross-sectional dimension is the diameter. We have found that in most practical applications, nanoparticles can be analyzed under the assumption of their spherical shape. Alternatively, non-spherical nanoparticles can be described using their other cross-sectional dimensions, such as the average diameter or principal axis length of elliptical or rod-shaped nanoparticles. The calculated "nanoparticle size" usually refers to the specific cross-sectional dimension of the nanoparticle that determines the motion of the nanoparticle, especially diffusion. Based on the physical laws of motion in liquids that govern the motion of nanoparticles, e.g., free diffusion, the nanoparticle size is calculated from nanoparticle trajectory data. In particular, the diffusion constant can be obtained from the nanoparticle trajectory motion data, and the nanoparticle size and optionally further nanoparticle characteristics can be determined with unprecedented precision and tolerance, not only in monodisperse nanoparticles but also in polydisperse mixtures of nanoparticles.

[0024] Furthermore, the inventors have found special advantages of iSCAT microscopy for determining nanoparticle size. Compared to conventional imaging methods such as dark-field imaging, iSCAT microscopy allows tracking of longer path lengths. The more steps the nanoparticle trajectory can be monitored, i.e., the more consecutive frames of images of the sample containing nanoparticles are collected, the more accurate the determination of nanoparticle size, particularly the diffusion constant, becomes. Therefore, imaging a sample via iSCAT microscopy can improve accuracy, speed, or both.

[0025] According to the present invention, the interference nanoparticle contrast, preferably the nanoparticle scattering cross-section, is calculated for each nanoparticle based on at least one detected iPSF feature. A further special advantage of the present invention is that the interference nanoparticle contrast, preferably the nanoparticle scattering cross-section, and the nanoparticle size are calculated independently of each other. Advantageously, two parameters, namely interference nanoparticle contrast and nanoparticle size, are obtained by creating a two-parametric nanoparticle scatter plot using a single iSCAT tracking measurement, enabling improved analysis. The inventors have found that the limitations of overlapping one-dimensional histograms of nanoparticle sizes obtained by conventional methods can be overcome by histograms obtained from two-parametric nanoparticle scatter plots. Two-parametric nanoparticle scatter plots allow for the identification of one or more populations of nanoparticles and even the analysis, particularly decomposition, of overlapping histograms. In the present invention, the histograms of size and contrast distributions can be combined, thus providing nanoparticle features with improved accuracy and reproducibility, and / or enabling the analysis of similar features, such as the nanoparticle distribution having an average diameter.

[0026] Advantageously, by combining iSCAT microscopy with NTA in particular, the inventors have discovered an all-optical method (interferometric NTA, iNTA) for highly sensitive and accurate determination of further characteristics such as nanoparticle size and optionally selected refractive index in a liquid environment. The inventors demonstrated the advantages of the present invention by characterizing samples of colloidal gold, polystyrene, and silica particles and comparing the results with those of conventional methods. Furthermore, the inventors demonstrated the ability to decode multicomponent samples and polydisperses, for example, extracellular vesicles in human urine and exosomes derived from the parasite Leishmania.

[0027] According to the present invention, multiple different frames from which images have been collected provide a set of iPSF features for each particle. Preferably, the step of calculating the interference nanoparticle contrast for each particle includes determining the interference scattering (iSCAT) contrast from each iPSF feature and determining a unique iSCAT contrast, in particular the maximum iSCAT contrast, among the iPSF features of each particle in different frames of the image, from which the scattering cross-section of each particle is calculated. Advantageously, utilizing a unique iSCAT contrast facilitates the calculation of the particle's scattering cross-section. Instead of using the maximum positive iSCAT contrast, another unique iSCAT contrast, such as the maximum negative iSCAT contrast, may be utilized.

[0028] A two-parametric nanoparticle scatter plot created according to the present invention is a map that extends in two dimensions (or axes) determined by the calculated nanoparticle size and the calculated nanoparticle scattering cross-section. The nanoparticle scatter plot may include at least one of a graphical representation (e.g., a printed or screen representation) and a data representation (e.g., a stored data field or table). Essentially, the calculated nanoparticle size and the calculated scattering cross-section can be directly used as the dimensions of the nanoparticle scatter plot.

[0029] Preferably, for each nanoparticle, the scattering cross-section is calculated from its interference nanoparticle contrast. The scattering cross-section can be calculated using calibration measurements performed with nanoparticles of known size and refractive index. Alternatively, the scattering cross-section can be fully calculated using well-determined setup-dependent parameters that can be measured separately.

[0030] Preferably, a function of the calculated nanoparticle size and interference nanoparticle contrast, preferably the scattering cross-section, is used as the dimension of the nanoparticle scatter plot. Thus, according to a preferred embodiment of the present invention, the plotted position of a particle in a two-parametric nanoparticle scatter plot is determined by the value of the function of the nanoparticle size and interference nanoparticle contrast, in particular the scattering cross-section of the nanoparticle, in particular the cube root of the interference nanoparticle contrast, and in particular the sixth root of the scattering cross-section of the nanoparticle. Utilizing a function of interference nanoparticle contrast, particularly the scattering cross-section, offers advantages in terms of increasing the resolution of nanoparticle positions in the nanoparticle scatter plot.

[0031] Advantageously, multiple nanoparticle properties to be determined are available. According to a more preferred embodiment of the present invention, the analysis step includes calculating at least one of the following: at least one mean nanoparticle size of the nanoparticles, at least one standard deviation of the nanoparticle size of the nanoparticles, at least one mean refractive index of the nanoparticles, and at least one standard deviation of the refractive index of the nanoparticles. Advantageously, for most applications of interest, each of these properties or any combination thereof allows for a sufficient characterization of the nanoparticles. As a further advantage, these properties or any combination thereof can be directly derived from a nanoparticle scatter plot or a histogram derived therefrom.

[0032] In a more preferred embodiment, more complex properties of the nanoparticles can be obtained. According to the first variant, the dimensions and / or refractive index of the multilayer structure of the nanoparticles can be calculated by utilizing generalized Mie theory and parameters of a given nanoparticle contained in generalized Mie theory. For example, parameters such as the number of layers and / or the refractive index of the thickness of some layers introduce assumptions and further information into the application of generalized Mie theory to determine the dimensions and refractive index of the layers involved. Nanoparticles having a multilayer structure have a core and at least one layer on the core. Advantageously, the present invention makes it possible to estimate or at least find the boundaries of the diameter and / or refractive index of the core and the thickness and / or refractive index of at least one layer. Based on deductive knowledge of the nanoparticles, e.g., their material (maybe more). Generalized Mie theory includes a general formula for electromagnetic scattering by nanoparticles, which represents the nanoparticle scattering cross-section dependent on the dimensions and refractive index of the nanoparticle layers. Alternatively or additionally, an effective surface layer generated in the suspension, in particular a hydrate layer accumulated on the nanoparticle surface, can be calculated.

[0033] According to a further advantageous embodiment of the present invention, the nanoparticles include at least two nanoparticle groups, wherein the average nanoparticle size, standard deviation of nanoparticle size, average refractive index, standard deviation of refractive index, average nanoparticle shape, and / or nanoparticle material of the nanoparticles of one group differ from the average nanoparticle size, standard deviation of nanoparticle size, average refractive index, standard deviation of refractive index, average nanoparticle shape, and / or nanoparticle material of the nanoparticles of the other group. In these embodiments, the step of analysis includes identifying the nanoparticle groups. In other words, different nanoparticle groups can be identified as separable distributions in a nanoparticle scatter plot. Advantageously, the nanoparticle groups can be identified even if their distributions of properties, such as size distribution, overlap, and / or even if there is only a slight difference in the average properties of the distributions, such as average nanoparticle size. As an example, gold nanoparticles with an average size of 10 nm and gold nanoparticles with an average size of 15 nm could be reliably separated using the method of the present invention.

[0034] Particularly preferably, not only is the group of nanoparticles identified, but the average nanoparticle size, standard deviation of nanoparticle size, average refractive index, standard deviation of refractive index, average nanoparticle shape, and / or nanoparticle material of the group of nanoparticles are also calculated.

[0035] According to a further advantageous embodiment of the present invention, a nanoparticle size histogram and a nanoparticle scattering cross-section histogram are created, and at least one of the histograms is decomposed. Preferably, the nanoparticle scattering cross-section histogram is created based on the sixth root value of the scattering cross-section of the nanoparticles. Alternatively or additionally, the steps to be analyzed may include creating a nanoparticle size histogram and an effective refractive index histogram, and decomposing at least one of the histograms. Creating the histograms includes providing a representation of the distribution of nanoparticle sizes occurring in the sample under investigation, e.g., a graphical representation, and a representation of the distribution of nanoparticle scattering cross-sections and / or effective refractive index. The histograms include the frequency distributions of nanoparticle sizes and nanoparticle scattering cross-sections and / or effective refractive index. Decomposing the histograms includes modeling the histograms by a fitting routine such as a Gaussian mixture model. The histograms may be analyzed using, for example, a Gaussian mixture model to extract various components of the nanoparticle mixture. A special advantage of the present invention is that the histogram is obtained by first creating a two-parametric nanoparticle scatter plot, and then the histogram is decomposed, thus facilitating the decomposition of the histogram.

[0036] According to further optional features of the present invention, advantages in terms of automation, processing speed, and reproducibility in determining nanoparticle properties can be obtained if the analysis step includes applying at least one of pattern recognition and machine learning-based data analysis to the distribution of plotted locations. Pattern recognition may include, for example, comparing the distribution of nanoparticle plotted locations with reference data from a known reference sample, recognizing a specific distribution shape and / or size, and identifying nanoparticle properties based on the properties of the known reference sample. Machine learning-based data analysis may include, for example, inputting a two-parametric nanoparticle scatter plot into a neural network trained with reference data from a known reference sample, and obtaining nanoparticle properties based on the output of the neural network.

[0037] Further advantages of the present invention arise from the wide range of available nanoparticles that can be investigated. Preferred examples include spherical nanoparticles, non-spherical nanoparticles, inorganic nanoparticles, organic nanoparticles, nanoparticles having a surface layer and / or nanoparticles having a multilayer structure.

[0038] A further advantageous embodiment of the present invention may provide a step of flowing a sample through the field of view of an interference microscope. Advantageously, the acquisition of a sequence of frames of images and tracking nanoparticles within the sequence of frames of images may be performed using a sample moving through the field of view, thus enabling an increased processing capacity for measurements. Preferably, a layered sample flow is utilized so as to maintain the advantage of calculating nanoparticle size from trajectory motion data.

[0039] Advantageously, multi-wavelength measurements can be performed, in which the step of acquiring a series of image frames is performed using illumination light having at least two different wavelengths, and the step of analyzing the distribution of nanoparticle plot positions is performed at different wavelengths. The step of acquiring a series of image frames can be repeated at at least two different wavelengths, or the illumination light can include at least two different wavelengths, the step of acquiring a series of image frames is performed once, and the scattered light superimposed with the reference light is acquired by spectral separation. In these variations, at least two nanoparticle scatter plots are acquired sequentially or simultaneously. Since the nanoparticle scattering cross-section depends on the wavelength of the scattered light, additional parameters for determining nanoparticle properties are acquired, thus improving the accuracy and reproducibility of the measurements.

[0040] In a further modification of the present invention, the acquired nanoparticle properties include spectral information of the nanoparticles. Preferably, this embodiment is used in conjunction with multi-wavelength measurements. The spectral information includes, for example, spectral absorption and / or transmission data of a sample containing nanoparticles or of the nanoparticles alone. Advantageously, the spectral information provides further characterization of the nanoparticles.

[0041] Preferably, sample fluorescence can be detected using an interference microscope, particularly to identify the material content of the nanoparticles. Detecting sample fluorescence may involve exciting the fluorescence with illumination light or additional excitation light and measuring the fluorescence spectrum or specific fluorescence bands of the nanoparticles. Advantageously, fluorescence detection allows for the identification of at least one substance contained in the nanoparticles.

[0042] According to further optional features of the present invention, the illumination light is linearly polarized. The polarization affects the scattering of the illumination light, and yet another parameter for determining the nanoparticle properties is obtained. Particularly preferably, the step of collecting a series of frames of images is performed with two orthogonal polarizations. Advantageously, the two polarizations can be recorded separately but simultaneously.

[0043] Advantageously, the method of the present invention may include a step of estimating the nanoparticle concentration, particularly the volume concentration, in a sample. Sequential frames of images, particularly trajectory motion data, provide the number of nanoparticles detected in the sample, and the sample volume can be estimated based on the imaging volume covered by the microscope. The nanoparticle concentration can be calculated from the number of detected nanoparticles and the imaging volume.

[0044] A more preferred feature of the present invention is a pulsed light source device that generates illumination light pulses having, for example, a pulse duration in the range of 10 fs to 1000 ns and a repetition frequency in the range of 1 kHz to 1000 MHz. Utilizing illumination light pulses may have advantages in providing high illumination intensity. Particularly preferably, a series of frames of an image are collected in synchronization with the illumination light pulses.

[0045] The method of the present invention can be combined with the determination of further properties of a sample and / or nanoparticles. According to a first variant, trajectory motion data is analyzed to determine the viscoelastic properties of the sample. Additionally or alternatively, according to a second variant, trajectory motion data is analyzed to determine the geometric shape of the nanoparticles. These variants of the present invention are preferably obtained by applying an external force, such as an optical field force or dielectric force, and / or a potential, such as an electric potential, which can influence particle motion beyond random diffusion. The particle trajectories obtained from the analysis of iPSF features provide information about the interaction of the particles with their environment and the properties of the latter, such as its viscoelasticity.

[0046] Advantageously, sample temperature can be used as a further parameter of measurement. Preferably, the step of acquiring a series of frames of images is performed at at least two different temperatures of the sample. Nanoparticle motion is dependent on the sample temperature, and motion data of multiple trajectories can be obtained from the series of frames of images.

[0047] According to further optional features of the present invention, if a step is provided to control the balance between the scattered light portion from the nanoparticles and the reference light portion, then an advantage in improving the signal-to-noise ratio of acquiring iPSF features can be obtained.

[0048] Features disclosed in the context of a method for determining nanoparticle properties and embodiments thereof also represent preferred features of the apparatus of the present invention for determining nanoparticle properties, and vice versa. Therefore, the above-described embodiments and preferred features of the present invention also apply to the apparatus, particularly with respect to the implementation of a method for determining nanoparticle properties. The above-described preferred embodiments, variations, and features of the present invention can be combined with each other as desired.

[0049] Further details and advantages of the present invention will be described below with reference to the accompanying drawings. [Brief explanation of the drawing]

[0050] [Figure 1] This is a schematic diagram of the features of an apparatus for determining the properties of nanoparticles according to a preferred embodiment of the present invention. [Figure 2] This is an explanatory diagram for the collection of trajectory motion data. [Figure 3A] This figure shows the experimental results of examining monodisperse particle samples. [Figure 3B] This figure shows the experimental results of examining monodisperse particle samples. [Figure 3C] This figure shows the experimental results of examining monodisperse particle samples. [Figure 3D] This figure shows the experimental results of examining monodisperse particle samples. [Figure 4A] This figure shows the experimental results of examining polydisperse particle samples. [Figure 4B] This figure shows the experimental results of examining polydisperse particle samples. [Figure 4C] This figure shows the experimental results of examining polydisperse particle samples. [Figure 4D] This figure shows the experimental results of examining polydisperse particle samples. [Figure 4E]This figure shows the experimental results of examining polydisperse particle samples. [Figure 4F] This figure shows the experimental results of examining polydisperse particle samples. [Figure 4G] This figure shows the experimental results of examining polydisperse particle samples. [Figure 5A] This figure shows further experimental results from the investigation of polydisperse and / or biological particle samples. [Figure 5B] This figure shows further experimental results from the investigation of polydisperse and / or biological particle samples. [Figure 5C] This figure shows further experimental results from the investigation of polydisperse and / or biological particle samples. [Modes for carrying out the invention]

[0051] Features of preferred embodiments of the present invention are described below with reference to its application for carrying out an apparatus and method for determining nanoparticle properties as shown in Figure 1. It should be noted that the implementations of the present invention are not limited to the configuration of the apparatus shown in exemplary manner. In particular, embodiments of the present invention may be modified with respect to the design of an interference microscope apparatus, especially its illumination and camera components, sample container, and optional further components such as fluorescence detection and / or transmittance measurement setups. In particular, iNTA may be combined with high-sensitivity fluorescence measurement to extract further information about the particles under investigation. The method of the present invention may be further modified by several means for increasing exposure time and signal-to-noise ratio, e.g., particle confinement strategies, shorter laser wavelengths, and higher laser power. These means allow access to high-resolution analysis of weakly scattering nanoparticles in a rapid, accurate, and non-invasive manner for a wide range of applications. Furthermore, measurements may be repeated at at least two different temperatures and / or at least two wavelengths of illumination light to improve the accuracy of determining nanoparticle properties.

[0052] The test apparatus 100 for determining nanoparticle properties, schematically shown in Figure 1, comprises, in particular, an interference microscope apparatus 110 which includes a coherent light source device 111, an imaging optical system 112, a sample container 113 containing a sample 1 having nanoparticles 2, and a detector camera device 114.

[0053] Typically, according to embodiments of the present invention, the test apparatus 100 further comprises a recording device 120 and an analysis device 130, which may be provided by a common computer unit or separate computer units. The recording device 120 is connected to a detector camera device 114 and configured to record images from the detector camera device 114. The analysis device 130 is arranged to track nanoparticles in the recorded images and to analyze the nanoparticle paths. At least the recording device 120, or both the recording device 120 and the analysis device 130, are coupled to the detector camera device 114. Furthermore, at least one of the recording device 120 and the analysis device 130 may be coupled to other components of the test apparatus, such as a light source device 111, to produce various illumination conditions. The illumination conditions may differ in particular with respect to the balance between the portion of scattered light from nanoparticles and the portion of reference light, and / or the power for controlling the illumination wavelength.

[0054] In the test apparatus 100, a series of frames of images are collected, processed, and analyzed. For this purpose, the sample 1 is illuminated with illumination light 3 from a coherent light source device 111. Scattered light from nanoparticles 2, superimposed with a non-scattered reference light, provides images collected over a predetermined exposure time. Using the analysis apparatus 130, the nanoparticles 2 are tracked within the series of frames of the image. Using the analysis apparatus 130, the interference point spreading function (iPSF) features of the nanoparticles 2 are established, and nanoparticle trajectory motion data is determined for each nanoparticle 2. Furthermore, using the analysis apparatus 130, the nanoparticle size and nanoparticle scattering cross-section of the nanoparticles 2 are calculated from the trajectory motion data, particularly from the iPSF features of the nanoparticles. In addition, a two-parametric nanoparticle scatter plot 200 (see, for example, Figure 4C) is created using the analysis apparatus 130, and the distribution of nanoparticle plot positions is analyzed using the analysis apparatus 130 to provide the nanoparticle characteristics to be obtained.

[0055] More specifically, the coherent light source device 111 is a low-coherent light source (laser diode, manufacturer: Lasertack, Germany) that generates illumination light 3 having an emission wavelength of 525 nm. The illumination light 3 is focused onto the back focal plane of the imaging optical system 112, which has a 63x oil immersion objective lens (NA 1.46, manufacturer: Zeiss, Germany), using lens 116. An ND filter 115 is positioned to adjust the illumination light power. A λ / 2 wave plate (not shown in Figure 1), located immediately after the ND filter 115, is used to match the polarization of the incident illumination light 3 that passes through the polarization-dependent beam splitter (PBS) 117. A λ / 4 wave plate 118 changes the polarization of the illumination light 3 from linear to circular.

[0056] The sample container 113 comprises a chamber formed by a microscope slide 113A, a coverslip 113B, and a spacer (e.g., a silicon gasket) between them. A sample 1 containing nanoparticles 2 (see enlarged schematic) is placed inside the sample container 113. Circularly polarized illumination light 3 is focused into the sample 1 using an imaging optical system 112. The focal plane of the imaging optical system 112 is typically positioned (e.g., several micrometers) above the coverslip and is preferably stabilized using active focus lock. In particular, to adjust the position of the focal plane above the coverslip 113B, the illumination light 3 is focused onto the coverslip 113B, and then a piezoelectric stage is used to position the focal plane (e.g., about 1 μm) above the coverslip 113B. To lock the focal position, a position-sensing detector (PDP90A) is used in combination with a red laser (CPS670F) operating in TIR mode and a PSD auto-aligner (TPA101).

[0057] Illumination light 3 is partially reflected by the coverslip (which provides the reference light) and partially scattered by the nanoparticles 2, reversing its polarity. The scattered light 4, superimposed with the reflected reference light, is collected using the imaging optical system 112. Upon passing through the λ / 4 wave plate 118, the polarization returns to linear, but rotated by 90°. The scattered light 4, superimposed with the reflected reference light, is reflected by the PBS 117 toward a detector camera device 114 equipped with a CMOS camera chip (e.g., type: MV1-D1024E-160-CL-12, manufacturer: PhotonFocus, Switzerland). As an example, 7 × 7 μm 2 A field of view (FoV) of 128 pixels × 128 pixels, corresponding to the sample area, is used. The recording speed, for example 5000 frames per second (fps), is typically limited by the camera's readout time. For example, by recording a series of frames of scattered light superimposed on a reference light over an exposure time of 1 second, trajectories with more than 100 localizations can be captured even for 10 nm nanoparticles 2, and trajectories with more than 1000 localizations can be captured for nanoparticles 2 larger than 20 nm.

[0058] As an example of practical measurement, for a frame rate of 5000fps, the exposure time is t exp The maximum possible value is set to =80μs. A 1-second sequence of 200 (or 600) image frames is recorded for monodisperse (or polydisperse) samples. More sequences of image frames (2300) may be recorded for diluted samples, such as urine samples (see below). For single-particle measurements, an image trigger included in the video acquisition software (pyLabLib Camcontrol) may be used to start saving frames 0.5 seconds before the particle crosses the center of the FoV. For polydisperse solution measurements, no trigger is used; rather, a sequence of image frames may be recorded continuously.

[0059] The collected frames of the image of sample 1 are processed using the analysis device 130. Processing the image frames includes determining nanoparticle trajectory motion data for each nanoparticle 2 using the analysis device 130, in particular determining the nanoparticle position and associated acquisition time sequence of each nanoparticle 2 obtained from each frame. Furthermore, as outlined below, the nanoparticle size is calculated from the trajectory motion data, and the nanoparticle scattering cross-section is calculated from the iPSF features of the nanoparticles using the analysis device 130.

[0060] The nanoparticle size (e.g., diameter) is calculated based on the diffusion constant D determined from the trajectory motion data. The diffusion constant D for nanoparticles in a liquid is described by the Stokes-Einstein (SE) equation.

number

[16] . This is where the iSCAT microscopy method used according to the present invention offers a decisive advantage due to its ability to track nanoparticles with high spatial accuracy and temporal resolution

[11] .

[0061] In practice, a diluted suspension of nanoparticles 2 is introduced into a closed chamber of the sample container 113. The nanoparticles 2 diffused in the sample 1 are imaged using a detector camera device 114. The trajectory length of the nanoparticles 2 is mainly limited by the axial diffusion of the nanoparticles. The diffusion constant D and the resulting d are extracted by fitting an MSD plot to the individual trajectories. For monodisperse samples, the average diffusion constant D - The localization error can be evaluated by fitting an average MSD plot weighted by trajectory length. For polydisperse samples, knowledge of interference contrast C is further utilized. Since interference contrast C is modulated axially as particles traverse the illumination region, the maximum positive contrast from each trajectory is preferably used for subsequent data analysis.

[0062] Figure 2A shows three examples of interference point broadening functions (iPSFs) resulting from the interference between a plane reference light wave (reflected from the sample interface) and a spherical wave (scattered by the particle) (

[11] ,

[17] ). The iPSFs shown in the left column of Figure 2A vary qualitatively depending on the positions of the particles relative to the coverslip 113B and the focal plane

[17] . To locate the iPSFs in a given image, a radial dispersion transform (RVT) can be applied to convert the iPSFs into bright spots, for example, as shown in the right column of Figure 2A

[18] . An example of a trajectory is superimposed in Figure 2B.

[0063] The nanoparticle scattering cross-section calculated for each nanoparticle is obtained from the iSCAT intensity derived as follows from the frame of the image. The iSCAT intensity recorded by the detector is expressed by the following equation,

Equation

[11] . The important feature that enables this is the use of a common-path configuration that is typically implemented in reflection mode

[11] . The iSCAT contrast is defined as follows.

Equation

Equation

[0064] The iSCAT signal intensity is expressed as interference contrast (C) and directly reports the scattering cross-section of the particle. For a uniform Rayleigh particle where k is the wavenumber and kd << 1, C of the central iPSF lobe is proportional to the polarizability α given by equation (2) (see

[10] ).

number

[0065] In the formula, V represents the particle volume, and n p and n m θ and θ are the refractive indices of the nanoparticle and its surrounding medium, respectively

[19] . For particles with kd greater than 1 or about 1 and for multilayer particles, generalized Mie theory explains the scattering intensity (see below). We have found that information about C can be used to decipher various species and determine their refractive indices in polydisperses.

[0066] Figure 3 shows experimental results obtained using commercially available monodisperse gold nanoparticles (GNPs) as a sample. Figure 3A shows the mean squared displacement (MSD) of GNP diffusion against delay time for GNP samples of different sizes. Thin lines represent the MSD extracted from each individual trajectory containing at least 25 localization events. Thick lines A-H represent the weighted linear average (by trajectory length) where free diffusion is confirmed. Diffusion constants extracted from the fitting are listed in the legend.

[0067] Figure 3B shows the nominal GNP diameter d provided by the manufacturer. nomFigure 3B shows the diffusion constants D of GNP for various diameters, extracted from the data. The dashed line shows the SE relation for T=21°C, and the solid line is the fitting of equation (1) to the SE relation. The agreement with the data is satisfactory (note the logarithmic scale), but the high precision of the measurement reveals a small bias in nanoparticle diameter. Preferably, the calculated nanoparticle diameter can be corrected for small deviations arising from, for example, the thickness of the hydration layer (

[20] ) and / or surfactant molecules. The correction of bias can be performed based on experimental tests using nanoparticles with nominal diameter or comparative samples. In fact, the solid curve in Figure 3B shows the radius I for all particles. H Considering the increase of 1.8 ± 0.3 nm, we report excellent agreement between theory and experiment.

[0068] To further investigate SE relation (1), measurements are performed at different temperatures using a microheating stage (VAHEAT, Interherence). As illustrated in the inset of Figure 3B for a GNP of 30 nm, there is a very good agreement between the experimental data (symbols) and the predictions of equation (1), d nom (Dashed line) Hydrodynamic diameter dH=d nom +2I H This is obtained when replaced with (solid line).

[0069] Figure 3C shows a histogram of nanoparticle diameters extracted from individual GNP trajectories according to SE relation (1). Individual measurements were weighted by their trajectory length. Gaussian fitting of the data established a normal distribution with a mean of d - mes And / or it is possible to determine the standard deviation σ(mes) (

[21] ). GNP data for 10 nm, 15 nm, 20 nm and 30 nm are recorded with an illumination power of 40 mW, and the rest are recorded with 2 mW. The inset in Figure 3C is I H And its error bars are shown. The dashed line is I obtained from the global fitting in Figure 3B. H This shows the value.

[0070] Figure 3D shows a comparison of the iNTA method of the present invention with various prior art methods. To compare iNTA with existing state-of-the-art methods, DLS (Zetasizer ZS90), NTA (Nanosight NS500), scanning electron microscope (SEM, Hitachi S-4800), and transmission electron microscope (TEM, Zeiss EM10) were used to characterize a GNP with a nominal diameter of 30 nm as an exemplary sample representing the lower limit of commercially available NTAs. The output of the DLS measurements represents the intensity-weighted distribution. The results clearly show that the DLS distribution and NTA size distribution have a greater spread than those of the SEM and TEM measurements. However, the width of the iNTA distribution is equal to the width of the TEM, and therefore combines excellent resolution with the advantages of optical measurement.

[0071] Figure 4 shows further experimental results obtained with a polydisperse nanoparticle sample using the conventional method (Figure 4A: DLS, Figure 4B: NTA) or the method of the present invention (Figures 4C-4G: iNTA). The polydisperse nanoparticle sample contains a mixture of three nanoparticle populations having GNPs of 15 nm, 20 nm, and 30 nm. The iNTA method of the present invention creates a two-parametric nanoparticle scatter plot 200 in which each nanoparticle 2 has a plot position determined by its calculated nanoparticle size and calculated nanoparticle scattering cross-section, and all nanoparticles 2 generate a distribution of nanoparticle plot positions.

[0072] In Figures 4C to 4G, the horizontal and vertical axes of the nanoparticle scatter plot 200 represent the measured diameter and the cube root of the iSCAT contrast, respectively. In each of Figures 4C to 4G, a 2D Gaussian mixing model is used to identify different populations. The lines drawn represent C and d due to their respective refractive indices. mes The relationship between the two is established, while the shaded areas indicate uncertainties in the refractive index data. The cross marks in Figures 4C to 4G represent the median of each distribution of the nanoparticle plot positions.

[0073] Figure 4A shows the intensity-weighted distribution of DLS measurements (Zetasizer ZS90), which produces a continuous, featureless distribution representing suspensions containing GNP at 15 nm, 20 nm, and 30 nm. As shown in Figure 4B, DFM-based NTA (Nanosight NS500) also does not decompose different populations.

[0074] The two-parametric nanoparticle scatter plot 200 in Figure 4C shows the iSCAT contrast C of individual trajectories extracted using iNTA and the measured nanoparticle diameter d. mes This shows that a visual inspection of the data clearly reveals three clusters 201, 202, and 203 corresponding to three nanoparticle populations. In fact, the histogram of iSCAT contrast C values ​​plotted on the right-hand vertical axis also decomposes the three populations in itself. The application of a two-dimensional (2D) Gaussian mixture model (GMM) with perfect covariance

[22] provides the three nanoparticle populations in a quantitative manner, and d mes Identifying groups within the histogram.

[0075] Figures 4D and 4E show further examples in which iNTA completely dissolves a mixture of 10 nm and 15 nm GNPs and a mixture of 40 nm and 60 nm PS spheres (PSSs), respectively, but particles in this size range are not typically accessible by DFM-based methods

[23] .

[0076] As shown in Figure 4F, for a mixture of 40 nm GNP, 100 nm PSS, and 100 nm silica beads (SB), similar sizes or σ sca When the particles are analyzed, C and d of the nanoparticle scatter plot 200 mes The advantages of combining knowledge become even clearer. C and d mes Although neither alone provides reliable information about the composition of the sample, their combination in the 2D nanoparticle scatter plot 200 provides solid evidence for the presence of three different populations (species). Furthermore, the application of GMM analysis is shown to be useful for C histograms and d mesThis allows for the decomposition of the histogram. The horizontal spread of the data cloud in the distribution in Figures 4C and 4D is due to uncertainties arising from short trajectories or localization errors, whereas the diagonal extension of the data in Figures 4E and 4F represents the true size distribution of the sample.

[0077] Further nanoparticle properties, such as the refractive index (RI) of the nanoparticles, can be obtained. In particular, based on equation (2), the measured iSCAT contrast C and the measured nanoparticle diameter d can be obtained. mes This provides direct access to the RI of nanoparticles. As an example, to fit the data in Figures 4C, 4D and 4F, gold (n AU An RI of (=0.63 + 2.07i) is used, and a solid line is obtained. The horizontal intercept reaches 1.6 nm, 1.8 nm, and 1.5 nm for each of the three cases, with a hydrated shell of 2l. H This provides another independent measure of C and σ. This information is based on experimentally measured C and σ. sca The expected value of can be used to relate it to a single calibration parameter for setup.

[0078] Furthermore, fitting the PSS and SB data in Figures 4E and 4F shows good agreement with the literature values, n PS =1.62 and n Si =1.45 is provided. For PSS and SB, the σ of larger nanoparticles sca It should be noted that the RI curve calculated from the complete Mie theory

[19] deviates from a straight line as it begins to include contributions from higher-order multipoles.

[0079] As a further experimental test, more complex mixtures of 10 nm and 20 nm GNPs were examined with and without polyethylene glycol coating (Creative Diagnostics; PEG MW 3,000). Figure 4G demonstrates the high performance of iNTA by clearly distinguishing four groups 201, 202, 203, and 204. Furthermore, the measurements provide a direct evaluation of the PEG layer thickness, which in this case corresponds to approximately 12 nm. These results pave the way for further highly sensitive and quantitative investigations of the interaction between composite nanoparticle samples and the surrounding liquid phase.

[0080] The superior sensitivity and resolution of iNTA measurements enable not only the examination of monodisperse and known polydisperse nanoparticles but also the investigation of real-world field problems. In fact, there are a number of applications where nanoparticles of various materials and sizes can be characterized rapidly, accurately, and non-invasively. The inventors tested the invention using the analysis of synthetically produced lipid vesicles, as well as extracellular vesicles (EVs) containing and / or bound to various proteins, nucleic acids, or other biochemical entities. EVs have been identified as conveyors for intercellular communication and disease markers, but known studies have been partially hampered by the processing capacity and resolution in their quantitative evaluation

[24] . EVs are often grouped as exosomes (arising from inside the cell, 30–150 nm in diameter) and microvesicles (originating from the cell membrane, 100–1000 nm in diameter), while particles smaller than 150 nm are sometimes called small extracellular vesicles (sEVs). The present invention was tested by investigating synthetically produced liposomes, parasitic extracellular molecules (EVs), and human urinary EVs, as described below.

[0081] Figure 5A shows the results of the iNTA measurement of the present invention on a sample of synthetically produced liposomes filtered to remove particles larger than approximately 200 nm. The liposome consists of a lipid bilayer shell 2A surrounding an aqueous interior (see inset in Figure 5A), and therefore the thickness of the shell (t sh ) and RI(n sh ) and internal RI(n inThis can be modeled by generalized Mie theory

[18] , which takes into account the following:

[0082] The lines drawn in Figure 5A confirm that the model based on these values ​​fits the entire experimental data very well. To explain the sensitivity of the results to variations within liposomes, a larger n in However, for the same shell parameters, a dashed curve is plotted in Figure 5A. Note that the high SNR of iNTA allows for the clear distinction of a simple model of a uniform nanosphere.

[0083] As a further example, we examined the parasitic EVs that cause leishmaniasis, a potentially deadly tropical disease. Leishmania parasites secrete numerous pathogenic factors, mostly transmitted along with small RNA molecules within the EVs. Quantitative characterization of vesicles released by leishmania (LEV) would be of great value in understanding their role in the infection process, but reliable data are lacking. Figure 5B presents iNTA nanoparticle scatter plot of LEVs. The size histogram is consistent with published results using DLS and NTA

[25] . However, the iNTA data provides access to more quantitative insights. Shell model and constant C isosurfaces and d marked in Figure 5B mes Based on the isosurface tests, the inventors have found that n in However, t sh ∈(3,8)nm and n sh We found that the narrow interval (1.334, 1.38) with ∈(1.44, 1.54) remains sufficiently constrained, explaining the various lipid shell thicknesses and the protein content of up to 60% within the shell. in This estimate means that it is sparsely packed with particles of different sizes and is mostly made up of water.

[0084] Extracellular vesicles are typically classified into two classes: exosomes and microvesicles, which have different cellular origins. Nevertheless, the smooth and limited 2D nanoparticle distribution in Figure 5B indicates that all LEVs have a similar consistency. To further elucidate this, n sh = 1.44, n in = 1.363 and t sh The solid line shown is an example obtained for 5nm. The curve reproduces the data trend very well, but the greater spread at C compared to the pure liposome data (see Figure 5A) provides an entry point to quantitative studies of parasitic secretory activity. sh , n sh or n in To reveal the detectable fluctuations of n. sh =1.44 and t sh Assuming a refractive index of 5 nm and an effective refractive index of 1.6 for protein material, the protein content of the EV internal solution is estimated to be approximately 10% ± 3%, as shown by the dotted curve in Figure 5B.

[0085] As a further example, human urinary vesicles (EVs) were examined. Urine is known to contain EVs, and EVs are expected to have great potential as disease markers. Urine was analyzed by NTA (

[26] ,

[27] ), giving a unimodal vesicle size distribution in the range of 50–300 nm. However, as shown in Figure 5C, although the iNTA size histogram also presents a continuous and broad distribution, its 2D nanoparticle scatter plot 200 clearly decomposes various subpopulations. In fact, the observed branching of the current data distribution, starting at approximately d=100 nm, sheds new light on EVs that were previously simply grouped as microvesicles.

[0086] Based on the assumption that all detected nanoparticles are EVs, constant C and d for the seven points marked in Figure 5C mes Calculating the isosurfaces shows that the lower cloud 201 represents EVs that are mostly water. However, the large and small particles on the oblique cloud 202 are nin It has a larger value. Together, the data allows us to define approximately three classes of particles: sEVs or exosomes with a size distribution of 50-150 nm that constitute more than 50% of the total EVs; microvesicles with a size distribution of 100-250 nm that have high protein content (large scattering cross-section); and microvesicles with a size distribution of 80-300 nm and very low biological content. The specific iNTA plot of urine 200 provides a basis for quantifying the composition of EVs and demonstrates the advantageous potential of the present invention for exploring disease-induced deviations.

[0087] Advantageously, by combining interference scattering microscopy and nanoparticle tracking analysis, iNTA pushes the limits of sensitivity, accuracy, and resolution in determining the size and / or refractive index of nanoparticle mixtures. The inventors demonstrated the capabilities of iNTA not only by detecting weaker-scattering nanoparticles than previously reported, but also by decoding complex nanoparticle species in various polydisperses and measuring the hydration layer of nanoparticles, such as colloidal gold nanoparticles. Furthermore, the inventors showed that the current performance of iNTA can shed new light on medical diagnostics.

[0088] The features of the present invention disclosed in the above description, drawings, and claims may be important individually, in combination, or in partial combination for implementation of the invention in its different embodiments.

Claims

1. A method for determining the nanoparticle properties of nanoparticles (2) contained in a sample (1), The aforementioned nanoparticle properties are, The nanoparticle size (d) of the aforementioned nanoparticle (2), The interference nanoparticle contrast (C) of the aforementioned nanoparticles (2), and, The scattering cross-section (σ) of the aforementioned nanoparticle (2), The effective refractive index (n) of the aforementioned nanoparticle (2), The average size of at least one of the nanoparticles (2), The nanoparticle size of the aforementioned nanoparticles (2) is at least one standard deviation, The average refractive index of at least one of the nanoparticles (2), At least one standard deviation of the refractive index of the nanoparticles (2), and the dimensions and / or refractive index of the multilayer structure of the nanoparticles (2) at least one of Includes, - A step of collecting a series of frames of an image by using an interference microscope (110), wherein the sample (1) is illuminated by illumination light (3) from a coherent light source (111), and the image is generated by scattered light (4) from the nanoparticles (2) superimposed with a non-scattered reference light, wherein the scattered light and the reference light have wavelengths greater than the cross-sectional dimensions of the nanoparticles (2), - A step of tracking the nanoparticles (2) within a series of frames of the image, wherein at least one interference point spreading function (iPSF) feature is established for each of the nanoparticles (2), and nanoparticle trajectory motion data, including the position of the nanoparticles within each frame, is determined for each nanoparticle (2). - For each nanoparticle (2), the step of calculating the nanoparticle size (d) from the trajectory motion data of the nanoparticle, - For each nanoparticle (2), the step of calculating the interference nanoparticle contrast (C) from at least one iPSF feature of the nanoparticle, - A step of creating a two-parametric nanoparticle scatter plot (200), wherein each nanoparticle (2) has a plot position based on its calculated nanoparticle size (d) and the calculated interference nanoparticle contrast (C), and all nanoparticles (2) generate a distribution of nanoparticle plot positions. - A step of analyzing the distribution of the plotted positions of the nanoparticles in order to provide the nanoparticle properties, Includes, The step of analyzing the distribution of the nanoparticle plot positions is as follows: - Identify one or more groups of nanoparticles from the nanoparticle plot positions in the nanoparticle scatter plot (200), To directly estimate the properties of the nanoparticles from the aforementioned nanoparticle plot locations, Estimating the nanoparticle properties from the histogram of the nanoparticle plot location, including creating a nanoparticle size histogram and a nanoparticle contrast histogram, and decomposing at least one of the histograms. and estimating the properties of the nanoparticles from the plotted position of the nanoparticles or from numerical simulations of the data derived therefrom. at least one of including, method.

2. The step of directly estimating the properties of the nanoparticles from the nanoparticle plot location, - The plot position is determined by the nanoparticle size (d) and the value of the function of the maximum interference nanoparticle contrast (C) of the nanoparticles (2). The method according to claim 1.

3. The plot position is determined by the value of the cube root of the nanoparticle size (d) of the nanoparticles (2) and the maximum interference nanoparticle contrast (C). The method according to claim 2.

4. The aforementioned analysis step is, - Calculate the average nanoparticle size (d) of at least one of the nanoparticles (2), - Calculate at least one standard deviation of the nanoparticle size (d) of the nanoparticles (2), and - Calculate the dimensions of the multilayer structure of the nanoparticles (2) by utilizing generalized Mie theory and predetermined nanoparticle parameters included in the generalized Mie theory. Includes at least one of the following: The aforementioned generalized Mie theory includes a general formula for electromagnetic scattering by nanoparticles, which represents the nanoparticle scattering cross-section that is dependent on the dimensions and refractive index of the nanoparticle layer. The method according to claim 1 or 2.

5. - A step of calculating the scattering cross-section (σ) for each nanoparticle (2) from its interference nanoparticle contrast (C). The method according to any one of claims 1 to 4, including the method described in any one of claims 1 to 4.

6. The step of directly estimating the properties of the nanoparticles from the nanoparticle plot location, - The plot position is determined by the value of a function of the nanoparticle size (d) and the scattering cross-section (σ) of the nanoparticles (2). The method according to claim 5.

7. The plot position is determined by the nanoparticle size (d) of the nanoparticle (2) and the sixth root of the scattering cross-section (σ), The method according to claim 6.

8. - For each nanoparticle (2), the effective refractive index (n) is determined using generalized Mie theory based on its size and scattering cross-section (σ). Includes, The aforementioned generalized Mie theory includes a general formula for electromagnetic scattering by nanoparticles, which expresses the nanoparticle scattering cross-section depending on the dimensions and refractive index of the nanoparticle layer. The method according to claim 5 or 6.

9. - The plot position is determined by the nanoparticle size (d) and the effective refractive index (n) of the nanoparticles (2). The method according to claim 8.

10. The aforementioned analysis step is, - Calculate the average refractive index of at least one of the nanoparticles (2), - Calculate at least one standard deviation of the refractive index of the nanoparticles (2), and - Calculate the refractive index of the multilayer structure of the nanoparticle (2) by utilizing generalized Mie theory and predetermined nanoparticle parameters included in the generalized Mie theory. The method according to claim 8 or 9, comprising at least one of the following.

11. The aforementioned analysis step is, - Calculate the surface layer accumulated on the surface of the nanoparticles. The method according to any one of claims 1 to 10, including the method described in any one of claims 1 to 10.

12. The surface layer is a hydrated layer. The method according to any one of claims 11.

13. The nanoparticles (2) include at least two groups of nanoparticles, - The average nanoparticle size (d), standard deviation of the nanoparticle size (d), average refractive index, standard deviation of the refractive index and / or nanoparticle material of the nanoparticles (2) of one group of the nanoparticle group is different from the average nanoparticle size (d), standard deviation of the nanoparticle size (d), average refractive index, standard deviation of the refractive index and / or nanoparticle material of the nanoparticles (2) of another group of the nanoparticle group, and - The analysis step includes identifying the nanoparticle group as a separable subdistribution in the nanoparticle scatter plot. The method according to any one of claims 1 to 12.

14. The aforementioned analysis step is, - Calculate the average nanoparticle size (d), the standard deviation of the nanoparticle size (d), the average refractive index, the standard deviation of the refractive index, and / or the nanoparticle material of the nanoparticle group. The method according to claim 13, including the method described in claim 13.

15. The aforementioned analysis step is, - Creating a nanoparticle size (d) histogram and an interference nanoparticle contrast (C) histogram, and decomposing at least one of the histograms. The method according to any one of claims 1 to 14, including the method described in any one of claims 1 to 14.

16. - The interference nanoparticle contrast (C) histogram is created based on the cube root value of the interference contrast (C) of the nanoparticles. The method according to claim 15.

17. The aforementioned analysis step is, - Creating a nanoparticle size (d) histogram and a nanoparticle scattering cross-section (σ) histogram, and decomposing at least one of the histograms. The method according to any one of claims 5 to 16, including the method described in any one of claims 5 to 16.

18. The nanoparticle scattering cross-section (σ) histogram is created based on the sixth root value of the scattering cross-section (σ) of the nanoparticles. The method according to claim 17.

19. The aforementioned analysis step is, - Creating a histogram of nanoparticle size (d) and a histogram of effective refractive index (n), and decomposing at least one of the histograms, The method according to any one of claims 7 to 18, including the method described in any one of claims 7 to 18.

20. The aforementioned analysis step is, - Applying pattern recognition to the distribution of plot positions, and - Applying machine learning-based data analysis to the aforementioned distribution of plot locations. The method according to any one of claims 1 to 19, comprising at least one of the following.

21. The aforementioned nanoparticles (2) are - Nanoparticles having specific dimensions in the range of 5 nm to 500 nm (2) • Spherical nanoparticles (2) - Non-spherical nanoparticles (2), • Inorganic nanoparticles (2) • Organic nanoparticles (2) - Nanoparticles having a surface layer (2), and • Nanoparticles having a multilayer structure (2) The method according to any one of claims 1 to 20, comprising at least one of the following.

22. - A step of flowing the sample (1) through the field of view of the interference microscope device (110), A method according to any one of claims 1 to 21, including the method described in any one of claims 1 to 21.

23. - The step of collecting a continuous frame of the interference pattern is performed using illumination light having at least two different wavelengths, and - The step of analyzing the distribution of the nanoparticle plot positions is performed at different wavelengths by using the nanoparticle scattering cross-section which depends on the wavelength of the scattered light. The method according to any one of claims 1 to 22, wherein multi-wavelength measurement is performed.

24. - The nanoparticle properties include spectral information of the nanoparticle (2), The method according to any one of claims 1 to 23.

25. - The step of detecting sample fluorescence using the interference microscope device (110) equipped with at least one spectral filter. The method according to any one of claims 1 to 24, including the method described in any one of claims 1 to 24.

26. The method according to claim 25, wherein the step of detecting the fluorescence of the sample is provided for identifying the material content of the nanoparticles (2).

27. - The illumination light is linearly polarized. The method according to any one of claims 1 to 23.

28. The step of collecting a series of frames of the aforementioned image is performed using two orthogonal polarizations. The method according to claim 27.

29. - A step of estimating the nanoparticle concentration in the sample (1), The method according to any one of claims 1 to 28, including the method described in any one of claims 1 to 28.

30. The coherent light source device is a pulse light source device (111) that generates illumination light pulses, and the frames of the image are collected in synchronization with the illumination light pulses. The method according to any one of claims 1 to 29.

31. - To analyze the trajectory motion data in order to determine the viscoelastic properties of the sample (1), and - Analyzing the trajectory motion data in order to determine the geometric shape of the nanoparticles (2), The method according to any one of claims 1 to 30, comprising at least one of the following.

32. - The step of collecting a series of frames of the image is performed at at least two different temperatures of the sample (1). The method according to any one of claims 1 to 31.

33. - A step of controlling the balance between the portion of scattered light from the nanoparticles (2) and the portion of reference light, The method according to any one of claims 1 to 32, including the method described in any one of claims 1 to 32.

34. The aforementioned at least one iPSF feature is, - iPSF contrast, i.e., the height of the central lobe of the iPSF, - Integrated iPSF, i.e., the overall brightness of the iPSF, and - iPSF shape, that is, the shape characteristics of the central lobe and side lobes of the iPSF, The method according to any one of claims 1 to 33, comprising at least one of the following.

35. - The interference nanoparticle contrast (C) is interference scattering (iSCAT) contrast. The method according to any one of claims 1 to 34.

36. A test apparatus (100) configured to determine the nanoparticle properties of nanoparticles (2) contained in a sample (1), The aforementioned nanoparticle properties are, The nanoparticle size (d) of the aforementioned nanoparticle (2), The interference nanoparticle contrast (C) of the aforementioned nanoparticles (2), and, The scattering cross-section (σ) of the aforementioned nanoparticle (2), The effective refractive index (n) of the aforementioned nanoparticle (2), The average size of at least one of the nanoparticles (2), The nanoparticle size of the aforementioned nanoparticles (2) is at least one standard deviation, The average refractive index of at least one of the nanoparticles (2), At least one standard deviation of the refractive index of the nanoparticles (2), and the dimensions and / or refractive index of the multilayer structure of the nanoparticles (2) at least one of Includes, - An interference microscope apparatus (110) comprising a coherent light source device (111), an imaging optical system (112), a sample container (113), and a detector camera device (114), wherein the coherent light source device (111) is arranged to illuminate the sample (1) in the sample container (113) with illumination light, and the detector camera device (114) is arranged to collect a series of frames of an image generated by superimposing scattered light from the nanoparticles (2) and a non-scattered reference light, wherein the scattered light and the reference light have wavelengths larger than the cross-sectional dimensions of the particles, - An analysis device (120) is provided for determining nanoparticle trajectory motion data of each nanoparticle (2), including the position of the nanoparticle in each frame, in order to establish at least one interference point spreading function (iPSF) feature of the nanoparticle (2), to track the nanoparticle (2) in the sequential frames of the image, and to establish at least one interference point spreading function (iPSF) feature of the nanoparticle (2). Equipped with, The analysis device (130) is further configured to calculate the nanoparticle size (d) from the trajectory motion data of each nanoparticle (2), to calculate the interference nanoparticle contrast (C) from at least one iPSF feature of each nanoparticle (2), to create a two-parametric nanoparticle scatter plot (200) in which each nanoparticle (2) has a plot position determined by its calculated nanoparticle size (d) and the calculated interference nanoparticle contrast (C), and all nanoparticles (2) generate a distribution of nanoparticle plot positions, and to analyze the distribution of nanoparticle plot positions for the purpose of providing the nanoparticle properties. The aforementioned analysis is, - Identify one or more groups of nanoparticles from the nanoparticle plot positions in the nanoparticle scatter plot (200), To directly estimate the properties of the nanoparticles from the aforementioned nanoparticle plot locations, Estimating the nanoparticle properties from the histogram of the nanoparticle plot location, including creating a nanoparticle size histogram and a nanoparticle contrast histogram, and decomposing at least one of the histograms. and estimating the properties of the nanoparticles from the plotted position of the nanoparticles or from numerical simulations of the data derived therefrom. at least one of A test apparatus including a test device.

37. The test apparatus according to claim 36, configured to perform the method described in any one of claims 1 to 35.

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