Acoustic droplet ejection of non-Newtonian fluids

Abstract

A method for ejecting droplets containing a non-Newtonian fluid with an acoustic droplet ejector can include applying tonebursts of focused acoustic energy to a fluid reservoir containing the non-Newtonian fluid at an amplitude sufficient to cause droplet ejection according to a toneburst pattern. The toneburst pattern can include three discrete toneburst segments, with a first toneburst segment having a longer duration than the second and third segments, and the third segment having a longer duration than the second segment. The exact duration and amplitude of the toneburst segments can be adjusted to affect the ejection characteristics.

Description

[Background technology]

[0001] Many biological solutions, polymer solutions, and polymer melts are inherently non-Newtonian fluids, as are many commonly found substances such as ketchup, starch suspensions, paints, honey, blood, custard, and shampoo. For Newtonian fluids, the relationship between shear stress and strain rate is linear, and the proportionality constant is the viscosity coefficient. An example of Newton's law of viscosity (1-D momentum transfer) is shown below in equation [1]:

number

[0002] For non-Newtonian fluids, the relationship between shear stress and strain rate may be non-linear and time-dependent. Therefore, a constant coefficient of viscosity cannot be defined. Non-Newtonian fluids are therefore defined as fluids that have a non-linear response in the shear stress profile (or the derivative in velocity with respect to the direction of fluid transport). Non-Newtonian fluids are also referred to as viscoelastic fluids because they have both viscous and elastic components in terms of the shear stress profile achieved when the fluid is sheared in the direction of fluid flow. These fluids are characterized by the following equation [2], where the apparent viscosity is not constant for non-Newtonian fluids:

number

[0003] There are various types of non-Newtonian fluids, for example, power law fluids can be either pseudoplastic (shear rate thinning), where η (apparent viscosity) decreases as shear rate increases, or dilatant (shear rate thickening), where η (apparent viscosity) increases as shear rate increases. Non-Newtonian fluids are best studied through several other rheological properties (in addition to measuring viscosity) related to stress and strain rate tensors under many different flow conditions (e.g., oscillatory shear flow or extensional flow measured using different devices or rheometers). This property is better studied using tensor-valued constitutive equations common in the field of continuum mechanics.

[0004] Rapid transport of Newtonian fluids can be achieved using a variety of techniques, including rapid transport by acoustic droplet ejection (ADE), as described, for example, in RGSteams and SAQureshi, “Method for Acoustically Ejecting a Droplet of Fluid from a Reservoir by an Acoustic Fluid Ejection Apparatus” (U.S. Patent No. 9,908,133, March 6, 2018). However, problems arise when attempting to transport non-Newtonian fluids, such as polymer solutions, using ADE. For example, problems include the apparent alignment of polymer chains that creates a stiffening effect for polymer solutions in elongational flows.

[0005] The problem can be subdivided into governing regimes where the "relaxation times" defined by viscoelastic parameters can be investigated based on the concentration of the polymer in the solution as well as the size (chain length) of the polymer itself. In the theory of polymer solutions, these regimes have different dominant chemical interactions that occur and can be manifested in different physics (e.g., visible with a high-speed camera), and these regimes are characterized by a range from dilute polymer solutions to concentrated polymer solutions to polymer melts. Also, the solvent system has a large driving force when it comes to governing the interactions and solution chemistry between the polymer chains and the solvent type (good, bad, theta), etc.

[0006] In certain applications, DNA solutions can be considered as non-Newtonian fluids. For example, one laboratory approach to studying DNA is to provide genomic DNA (gDNA) in a buffer system (typically TE, PBS, water, etc.) without attempting to "condense" the polymer, which forms a polymer-solvent system that is non-Newtonian. Adding an additional complication, the properties of the fluid may change depending on the concentration of the solution. For a dilute polymer solution in a solvent system (native gDNA, as an example), there is little interaction between the native gDNA strands, resulting in a simplified system that can be modeled and characterized by relaxation time, maximum extension, and zero shear viscosity. At higher native gDNA concentrations, modeling such a polymer-solvent system becomes more complex. The relaxation time now depends not only on the strand length but also on the concentration, and grows exponentially. The maximum extension is also related to the gDNA concentration, but the growth is limited. In summary, additional processing methods for DNA analysis are necessary, but the problem of characterizing the fluid properties of non-Newtonian DNA-containing solutions has been too complex to date to enable general DNA-containing fluid transfer by known techniques in ADE. Thus, there is a need for novel ADE processes that can reproducibly, accurately, and precisely transfer native gDNA and other viscoelastic non-Newtonian fluids. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] U.S. Pat. No. 9,908,133 [Brief description of the drawings]

[0008] Various embodiments according to the present disclosure will now be described with reference to the drawings.

[0009] [Figure 1] 1 shows a set of high speed images of a droplet ejection instrument ejecting under under power and under power. [Diagram 2] 1 is an amplitude plot showing the acoustic waveforms of Newtonian and non-Newtonian tonebursts. [Figure 3-1] 1 illustrates a water dispensing process utilizing a first acoustic toneburst. [Figure 3-2] 13 shows the ejection process of a solution containing human native gDNA in the second tone burst. [Figure 4-1] 1 shows a set of acoustic signal waveform patterns from mixtures of water and human native gDNA at increasing gDNA concentrations. [Figure 4-2] 1 shows a set of acoustic signal waveform patterns from mixtures of water and human native gDNA at increasing gDNA concentrations. [Figure 4-3] 1 shows a set of acoustic signal waveform patterns from mixtures of water and human native gDNA at increasing gDNA concentrations. [Figure 4-4] 1 shows a set of acoustic signal waveform patterns from mixtures of water and human native gDNA at increasing gDNA concentrations. [Diagram 5] 13 is an image showing sample transfer efficiency of human native gDNA under Newtonian ejection power regimes and increasing power regimes. [Figure 6] 13 is an image showing sample transfer efficiency of human native gDNA under Newtonian ejection power regimes and increasing power regimes. [Figure 7] 1 is a chart showing the transfer success rate of various human native DNA solutions based on ejection power. [Figure 8-1] 1 shows human native gDNA fragment sizes and gDNA concentrations obtained from ADE transferred gDNA solutions using a Newtonian ejection power regime. [Figure 8-2] 1 shows human native gDNA fragment sizes and gDNA concentrations obtained from ADE transferred gDNA solutions using non-Newtonian ejection power regimes. [Figure 9] 1 is a diagram showing the crossing points (Cp) of a series of DNA concentrations indicating the relative success of transfer by ADE (Echo) compared to manual pipetting. [Figure 10-1] 1 is an analytical trace of a BIOLINE® gDNA sample. [Figure 10-2] 13 is an analysis trace of a PROMEGA® gDNA sample. [Figure 11] 1 is a plot showing Cp vs. log[DNA] of biological and technical replicates of native gDNA. [Figure 12] 12 is a plot showing the percent CV of the Cp curves shown in FIG. [Figure 13] 1 shows an analytical trace of a human gDNA sample showing large fragment sizes. [Figure 14] 1 is a plot showing Cp versus log[DNA] for a series of biological gDNA replicates. [Figure 15] FIG. 15 is a plot showing percent CV of Cp versus log[DNA] data for the replicate series described in FIG. 14. [Figure 16-1] 1 shows an analytical trace of a BIOLINE® DNA sample. [Figure 16-2] 1 shows an analytical trace of a PROMEGA® DNA sample. [Figure 16-3] A series of analytical traces for the first, second, third, and fourth human native gDNA samples are shown. [Figure 16-4]A series of analytical traces for the first, second, third, and fourth human native gDNA samples are shown. [Figure 16-5] A series of analytical traces for the first, second, third, and fourth human native gDNA samples are shown. [Figure 16-6] A series of analytical traces for the first, second, third, and fourth human native gDNA samples are shown. [Figure 17] 1 is a series of fluorescence and fluorescence derivative plots showing amplification curves and melt studies to assess the success of qPCR reactions. [Figure 18] 1 shows the time evolution of droplet formation of human gDNA solution. [Figure 19] FIG. 1 shows the time evolution of droplet formation for different samples of human gDNA solution and for different size scales. [Figure 20] 1 shows droplets transported with two different tone burst ratios at various acoustic power levels. [Figure 21] 11 is a chart comparing ejection volume versus ejection power for increasing ejection power for two different tone burst ratios. [Figure 22] FIG. 13 is a scatter plot of the measured volume transferred for both large and small drops at various ejection powers. [Figure 23] We show successful droplet transfer for shear thinning of non-Newtonian fluids. Note that attempting transfer using Newtonian parameters does not result in ADE transfer. [Figure 24] This shows successful droplet transfer for shear thickening of non-Newtonian fluids. Note that attempting transfer using Newtonian parameters does not result in ADE transfer. [Diagram 25] Schematic showing the layout map of the test plate used to quantitatively evaluate the robustness of DNA transfer by ADE. [Figure 26]FIG. 26 is a plot showing Ct vs. log[DNA] for human gDNA transferred from the test plate of FIG. 25 using small droplets. [Figure 27] FIG. 26 is a plot showing Ct vs. log[DNA] for human gDNA transferred from the test plate of FIG. 25 using large droplets. [Figure 28] 1 is a chart showing the power levels that successfully achieved linearity with the expected slope for each given starting concentration and drop count. [Figure 29] 1 illustrates a first exemplary process for setting droplet ejection parameters and ejecting droplets of a non-Newtonian fluid. [Diagram 30] 1 illustrates a second exemplary process for setting droplet ejection parameters and ejecting droplets of a non-Newtonian fluid. [Diagram 31] 13 illustrates a third exemplary process for setting droplet ejection parameters and ejecting droplets of a non-Newtonian fluid. [Diagram 32] 13 illustrates a fourth exemplary process for setting droplet ejection parameters and ejecting droplets of a non-Newtonian fluid. [Diagram 33] 1 illustrates an exemplary process for using Dynamic Mound Analysis (DMA) to characterize non-Newtonian fluids and establish fluid identity. [Diagram 34] 1 illustrates an exemplary process for using dynamic breakaway analysis (DBA) to achieve acoustic droplet ejection and fix toneburst parameters. [Diagram 35] 1 illustrates an exemplary process for using dynamic volume analysis to characterize the volume of droplets produced by acoustic droplet ejection and to fix the droplet volume. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] In the following description, various embodiments are described. For purposes of explanation, specific configurations and details are set forth to provide a thorough understanding of the embodiments. However, it will be apparent to one skilled in the art that the embodiments may be practiced in other configurations or without the specific details. Furthermore, well-known features may be omitted or simplified so as not to obscure the described embodiments.

[0011] The technology described herein utilizes acoustic droplet ejection (ADE) technology to enable the ability to transfer samples of non-Newtonian fluids (such as intact native genomic DNA or viscoelastic polymers in solution) in a highly reproducible, highly accurate and highly precise manner.

[0012] More specifically, the technology described herein allows for the transfer of untreated, unfragmented, intact native genomic DNA (collectively referred to as native gDNA) from wells in acoustically competent microplates or tubes using acoustic droplet ejection (ADE) technology. Currently, many biological assay workflows utilize more conventional methods of transferring untreated, unfragmented, intact gDNA, such as using chip-based systems. This methodology provides a truly pure, non-contact, touchless liquid transfer process for future laboratory settings for research use only or clinical diagnostic applications.

[0013] Specific improvements to the ADE technique described herein allow for the transfer of native gDNA samples with both large fragments (>20 Kb) and / or high concentrations (>100 ng / μL). Previous ADE capabilities were limited in that the fluid may deviate somewhat from Newtonian behavior before the transfer process would be accurate and precise enough for the intended application. For example, when forming nanoliter-scale droplets, if the gDNA size exceeds 20 Kb length and the concentration exceeds 100 ng / μL, the process of acoustic droplet formation will be hindered by the non-Newtonian nature of the fluid. The ability to transfer native gDNA without manipulations that reduce fragment size or physical size or concentration, such as dilution (e.g., buffers (Tris-EDTA), water, other diluents), additives (e.g., salts, surfactants), heating, shearing, fragmentation (enzymes, kits), condensation reagents (e.g., ionic liquids, spermidine, spermine), or other forms, is therefore highly desirable for transferring native gDNA samples that are pure and stored in repositories. This capability will enable researchers and clinicians to truly miniaturize sample handling (including nanoliter scale) of limited (and often precious) native gDNA samples, or other problematic biological or polymeric materials, for life science and clinical diagnostic applications. Furthermore, this new capability will reduce the number of steps in the automated platform workflow, as well as the need to utilize multiple different automated liquid handling platforms or instruments to perform further upstream steps.

[0014] The technology described herein allows users to work from a "pure" unspiked form of extracted native gDNA and acoustically transfer small volumes (few or tens of nanoliters) to enable the desired downstream biological assays. Large volumes can also be transferred taking advantage of the fact that the novel acoustic droplet transfer process is highly reproducible, accurate and precise over the range of volumes transferred, all due to the multiple small volume sample transfers that are highly reproducible, precise and precise. In this document, experimental results obtained from high speed cameras (VISION RESEARCH INC., PHANTOM), absorbent paper (SYNGENTA), and quantitative PCR (qPCR) (ROCHE and THERMOFFISHER) experimental results demonstrate the capacity, quality and quantity of native gDNA transferred according to the technology described herein. The reproducibility, accuracy and precision of all samples transferred is high and quantitatively demonstrated by the low coefficient of variation (CV) of Cp or Ct of the described qPCR assays utilizing housekeeping genes. Cp or Ct represents the crossing threshold by which DNA is amplified, or the value at which the "knee" lies in the acquisition of the amplification curve. It is a valuable metric of interest to molecular biologists who quantitatively examine the amount of DNA present in an assay.

[0015] Furthermore, due to the low shear rate of the non-Newtonian acoustic transport process, the gDNA remains intact after it is transported to a target vessel, such as a well or tube, where it is further utilized in downstream processes such as NGS.

[0016] Specific embodiments are described in detail below with reference to the figures.

[0017] According to at least one embodiment of the present invention, a novel ejection toneburst is disclosed (see Table 1 and FIG. 1) that works in concert with a non-trivial ejection power range (see FIG. 2). As typically the case for Newtonian fluids, the determination of the correct ejection power (combined with a standard aqueous buffer Newtonian toneburst) is determined by a signature (characteristic acoustic reflection) that is generated and correlated with the generated mound image signal processing (determining the null space) described, for example, in U.S. Pat. No. 9,908,133, which is incorporated herein by reference for all purposes. Mound image printing (MIP) results in the generation of highly repeatable, accurate, and highly precise sample transfers for acoustic droplet ejection at 1-3 dB ejection energy, defined relative to the ejection threshold for transfer, for Newtonian fluids. This process is used to determine the power requirements for ejecting droplets from a fluid reservoir, such as a well or tube, of Newtonian fluid. For non-Newtonian fluids, there are significantly different physicochemical properties when the fluid is subjected to extensional flow (as in the ADE process). Furthermore, MIP results are unable to discern these differences because perturbations related to signal processing occur at low shear values. Thus, the reliance on MIP during this step of signal processing to indicate correct ejection for transferring energy, as it exhibits insufficient differentiation for Newtonian fluids, suggests that in some cases, an ejection power of 1 dB for acoustic droplet ejection is not effective for droplet formation to compensate for stronger deviations from Newtonian behavior occurring at increased shear rates due to high acoustic power. ADE-based transfer for non-Newtonian fluids may be achieved using a new tone burst in conjunction with a new ejection energy regime (typically around 6-11 dB above the ejection threshold). Results are shown below for three types of intact gDNA samples, ranging from 1 dB (for Newtonian ADE) to 6-11 dB (for correct or non-Newtonian ADE) in concert with the new tone burst.

[0018] FIG. 1 shows a set of high speed images 100 of a droplet ejection instrument in underpowered (left-102, 104, 106) and overpowered (right-108, 110, 112) conditions resulting in successful ADE. The figures show the lack of treating a non-Newtonian fluid as a Newtonian fluid for purposes of ADE. In response to detecting that the fluid being transferred by ADE is a non-Newtonian fluid, according to an embodiment of the invention, the ADE system employs increased power conditions to compensate for the non-Newtonian nature of the solution in the test reservoir. For FIG. 1, a non-Newtonian gDNA solution was used. Images were captured at 20,000 frames per second with an exposure time of 49 μs. Three different samples of native gDNA were ejected from the acoustic tube. From top to bottom, samples were commercially obtained from BIOLINE® (102, 108) and PROMEGA® (104, 110), whereas human samples were extracted using Roche MagNA Pure® (106, 112). All samples were >20 Kb and / or high concentration >100 ng / μL. [Table 1]

[0019] Table 1 shows a comparison of tone burst segment lengths for Newtonian and non-Newtonian fluid calibrations used for successful ADE. Successful ADE on non-Newtonian samples as described herein was performed by modifying the Labcyte® Echo acoustic droplet ejector. This Newtonian tone burst forcefully transports 25 nL of Newtonian fluid (typically for buffer or aqueous solutions) on a Labcyte Echo 525 liquid handler. A non-Newtonian tone burst was used to transport long 20 Kb native gDNA at concentrations >100 ng / μL on a Labcyte Echo 550 / 555. Figure 2 shows an acoustic amplitude plot 200 showing the acoustic waveforms of Newtonian 201 and non-Newtonian 202 tone bursts.

[0020] Biological fluids, including intact native gDNA or viscoelastic polymer solutions and melts, commonly exhibit nonlinear responses to shear stress profiles as a function of strain rate and are well-known examples of non-Newtonian fluids that are desirable for operation in ADE. These fluid samples are complex macromolecular solutions whose properties are often difficult to characterize a priori using MIPs. In fact, both Newtonian and non-Newtonian fluids in reservoirs such as acoustically qualified microplates or acoustically qualified tubes show little or no difference in the signal processing and perceived energy required for the ADE process when information is obtained from standard acoustic echoes. Viscoelastic properties are manifested in the ejection energy, which causes the droplets (generated from a mound with suitable energy) not to pinch off. As a result, the droplets return to the mound and "snap back" into the reservoir of the sample in the well or tube. In the case of native gDNA, the solution can be "stretched" up to a length of up to 1 cm (or more) without droplet breakoff, in some cases. Higher applied energy results in tighter alignment of the polymer gDNA strands, making droplet breakaway more difficult. An analogy is the stretching of a rubber band, where the polymer strands become more aligned and more difficult to stretch. Furthermore, the increased viscoelasticity of non-Newtonian fluids adversely affects the balance of forces during droplet breakaway, and the new methodology described herein has been shown to enable highly repeatable, highly accurate, and highly precise acoustic droplet ejection for non-Newtonian fluids.

[0021] FIG. 3A shows the ejection process using optically strobed water, utilizing an acoustic toneburst voltage of 650 mV, at 1400 μs after the toneburst. FIG. 3B is a stroboscopic image of the ejection process of native gDNA with toneburst characteristics of toneburst segment 1 length=275 μs, segment 2 length=130 μs, segment 3 length=100 μs, segment 1 CF=5.85 MHz, segment 3 CF=5.15 MHz. The acoustic toneburst voltage is 1100 mV with the same Newtonian toneburst segment length. Images were captured approximately 5000 μs after the toneburst was applied. The ejection toneburst creates a leading protrusion 301 (FIG. 3A, FIG. 3B) that extends away from the reservoir and is connected to the rest of the fluid by a filament 303. It is useful to note that in the case of native gDNA, the DNA polymer strands align such that the elongation flow places the DNA in a "stretched" configuration that continues to elongate as more force is applied (see Figure 3B). Furthermore, as more energy is applied, longer native gDNA threads are created (several mm) and the droplets do not detach. At an acoustic power of 1400 mV (~6 dB higher than water ejection), droplet separation is still absent. Also, note that in these viscoelastic fluids, regular MIP measurements yield appropriate predicted ejection powers when no viscoelasticity is present in native gDNA and the strands do not significantly align until close to droplet detachment. Thus, MIP-based results fail to capture the full impact of the viscoelastic phenomenon.

[0022] In a typical acoustic inspection of a fluid-filled well or tube, an acoustic transducer is positioned beneath the well of interest. Water, or another coupling fluid, bridges the gap between the transducer and the bottom of a reservoir, such as the underside of a microplate well or the bottom of a tube. The acoustic signal propagates from the transducer, through the coupling fluid, through the plastic bottom membrane of the well or tube, into the fluid, and finally into the air above. The acoustic signal is reflected at each of these interfaces and collected by the same transducer that emitted the original signal. The initial waveform does not overlap with the reflected signal. In a process called mound image printing (MIP), information about the fluid properties is extracted from the reflected signal. For increasing gDNA concentrations (as shown in Figure 4A-D), the MIP results show no difference between pure buffer conditions (1xPBS) up to a native gDNA concentration of 100 ng / μL. Specifically, Figure 4A includes chart 400a showing acoustic signal waveform patterns from water containing no native gDNA, while Figures 4B, 4C, and 4D include charts showing acoustic signal waveform patterns from water containing native gDNA at 50 ng / μL (400b), 120 ng / μL (400c), and 160 ng / μL (400d), respectively. Above 100 ng / μL native gDNA concentrations, there is the presence of capillary waves manifesting at the surface perturbation resonance, which would indeed suggest that lower energy is required for droplet detachment.

[0023] According to the present disclosure, an alternative ejection toneburst or acoustic waveform is applied to a non-Newtonian fluid, such as native gDNA, contained within a reservoir, such as a well or tube (see FIG. 2). An acoustic transducer is positioned below the reservoir and focused at the surface of the fluid contained within the well or tube. The amplitude of the toneburst required to reach the surface of the fluid, disturb the surface, and detach a droplet is determined to be significantly different than a Newtonian fluid (e.g., water or 1xPBS). When an ejection toneburst with an amplitude in the range of 1-3V is applied to a well filled with water or 1xPBS, fluid breakup occurs at the surface and a droplet is ejected. This is the normal behavior expected for an ADE process operating with a Newtonian class fluid.

[0024] The effectiveness of acoustic release can be experimentally demonstrated using an Echo 550 / 555 liquid handler by performing sample droplet tests at various powers as shown in Figures 5 and 6. Figure 5 shows a visual / qualitative single 2.5 nL sample droplet test for transfer of 150 Kb of native gDNA at 100 ng / μL using increasing power, +0 dB (left), and nominal power +2.0 dB (right). Figure 6 shows a visual test for ADE transfer of a human sample of 40 Kb of native gDNA. The power on the left half of the water-sensitive paper was +0.0 dB and the power on the right half of the water-sensitive paper was +8.0 dB. At each of the 384 locations, one transfer of 2.5 nL was attempted. The presence or absence of a dot indicates a successful or unsuccessful droplet release event, respectively, as the water-sensitive paper changes color from yellow to blue when aqueous fluid comes into contact with the paper.

[0025] Highly viscoelastic native gDNA solutions (human sample in Figure 5 and commercial native gDNA sample in Figure 6) were attempted in two modes. First, a Newtonian tone burst and energy transfer profile (502, 602 - left side of the figure) was used, and then a non-Newtonian tone burst and energy profile (504, 604 - right side of the figure). Clearly, when using a Newtonian profile, the droplet of native gDNA does not transfer, and for the non-Newtonian profile, successful transfer is indicated by the water sensitive paper exhibiting a blue dot. This is a qualitative measure, and transfer failures have blank areas where blue dots are expected. Figure 5 shows the human sample of native gDNA, and Figure 6 shows the commercial BIOLINE® sample.

[0026] Secondly, as power increases, so does the number of successful transfers (see Figure 7), indicating that the "normal" parameter space does not incorporate successful or reproducible success (demonstrated later with qPCR results). The increase in the successful transfer rate at approximately 6-11 dB above the standard ejection threshold highlights a new operational regime suitable for non-Newtonian viscoelastic fluids in concert with the novel non-Newtonian tone burst. As highlighted in Figure 7, the normal parameters used for Newtonian fluids are not appropriate. This is the setting that represents the minimum energy required for successful droplet ejection of non-Newtonian fluids. The successful transfer rate is shown as a percentage of transfer for BIOLINE® samples (702), human gDNA samples (704), and PROMEGA® samples (706).

[0027] Figure 7 shows an empirical approach to determine the transfer success rate of various DNA solutions based on ejection power. Successful transfer is defined as when a droplet is acoustically ejected and reaches the destination plate (or water-sensitive paper attached to the destination plate as in Figures 5 and 6). Successful transfer is plotted as a function of power added to the MIP solution. The MIP feature is on and is used in this embodiment to account for changes in sample fill height. All transfers are performed using a fixed signature or impedance value of 1.48 MRayl.

[0028] Ideally, in biological assays and workflows, native gDNA is used as the starting input material. Typically, native gDNA is then manipulated or prepared for downstream applications. For example, native gDNA can be fragmented into short segments for next-generation sequencing (NGS) applications. State-of-the-art ADE techniques can reproducibly, accurately, and precisely transfer native gDNA fragments of 20 kilobases (Kb) or less at concentrations up to 100 ng / µL, because deviations from Newtonian behavior are low enough to allow accurate prediction of ejection power from simply forming a mound that will result in robust droplet detachment from the reservoir when a higher predicted power is applied. Both size (as measured as the average length of the native gDNA fragments) and concentration of native gDNA are known parameters with the ability to affect reproducible ADE, both individually and in combination contributing to deviations from Newtonian behavior. Results of studies on various native gDNA samples are displayed in Figure 8. Figure 8A shows that native gDNA less than 20 Kb can be acoustically reproducibly, accurately, and precisely transported (utilizing Newtonian parameters for tone burst and ejection energy). Furthermore, low concentrations up to 100 ng / μL were successfully transported using Newtonian parameters. Figure 8B shows that both larger sized (up to 150 Kb) native gDNA, and higher concentrations (up to 200 ng / μL) native gDNA can be reproducibly, accurately, and precisely transported using non-Newtonian parameters, essentially widening the two-dimensional spatial envelope of native gDNA size and gDNA concentration that can be acoustically accessed via ADE.

[0029] 8A and 8B are scatter plots showing gDNA concentration versus fragment length. Unsuccessful (802), partial (804), and successful (806) transfers were acquired at underpowered conditions and Newtonian tone burst power. Successful transfers (808) were acquired uniformly at high power conditions and using non-Newtonian tone bursts. Acoustic transfers are measured by qPCR (further analysis follows). This figure helps to show a larger envelope in the two-dimensional space of fragment size and native gDNA concentration.

[0030] The methods described herein allow for highly reproducible, highly accurate, and highly precise transfer of native gDNA with fragment sizes of over 20 Kb and bulk concentrations of over 100 ng / μL from acoustically qualified tubes (but can also be performed from acoustically qualified wells in microplates, reservoirs, or similar). Transferring long fragments of highly concentrated native gDNA in such small volumes (2.5 nL) and without physical contact from one container to another allows groundbreaking capabilities for clinical applications (diagnostics) to minimize processes, allowing users to do more with less without fear of cross-contamination or binding or reagents to the transfer device. These methods can ease workflows for the development and miniaturization of new assays, opening opportunities for research and clinical laboratories. Utilizing acoustic tubes and other reservoirs to transfer gDNA can increase the working range substantially to over 150 Kb and even to over 750 ng / μL.

[0031] To quantitatively assess gDNA transfer, quantitative polymerase chain reaction (qPCR) was utilized to subsequently confirm and detect the expression of the housekeeping gene beta-actin β-actin. qPCR assays were performed using the following: β-actin forward primer: AGC CAT GTA CGT TGC TAT CC; β-actin reverse primer: CGT AGC ACA GCT TCT CCT TAA T, (IDT). Assays were performed using either the Roche LightCycler® system or the THERMOFFISHER QuantStudio 6 Flex Real-Time PCR system. The Roche LightCycler® system utilized the LightCycler® 480 SYBR Green I Master (Roche), LightCycler® 480 Multiwell Plate 384 (Roche), and LightCycler® 480II Instrument (Roche). The qPCR program was used as follows: step 1, 95°C for 60 seconds, step 2, 95°C for 15 seconds, 60°C for 30 seconds, 72°C for 60 seconds, single collection (45 cycles), step 3, 95°C for 10 seconds, 60°C for 60 seconds, and 97°C continuously. The THERMOFISHER QuantStudio 6 Flex Real-Time PCR System utilized PowerUp™ SYBR® Master Mix, THERMOFISHER 384-Well Clear Optical Plate. The qPCR program was used as follows: step 1, 50°C for 2 minutes, 95°C for 2 minutes, step 2, 95°C for 15 seconds, 60°C for 1 minute, collection (40 cycles), step 3, 95°C for 15 seconds, 60°C for 1 minute, and 95°C for 15 seconds.

[0032] Control the data using Newtonian calibration for the ADE of fragmented gDNA and compare to hand-pipetted fragmented gDNA. Human native gDNA (56 Kb) commercially obtained from PROMEGA® was fragmented by shearing (to 8 Kb) using a sonicator (Corvallis Sonicator Technology) and manually pipetted into acoustically qualified tubes. An eight-point two-fold standard curve was generated using the 8 Kb fragment starting at 100 ng / μL as the highest concentration. For each standard curve point, 32 technical replicates were transferred and assayed by qPCR as described below: [Table 2]

[0033] For each standard curve point, all 32 replicates were transferred. The experimental results were tabulated and constitute the values ​​shown in Table 2. For each concentration of DNA, the mean crossing point (Cp) value, standard deviation of Cp, %CV of Cp, minimum and maximum Cp, and replicate number were determined. These results serve as a set of control experiments to show the expected variability (for small sized fragmented gDNA) of the acoustic transfer process with Newtonian parameters compared to the hand pipetted control experiment capabilities.

[0034] Figure 9 shows an R of ≥ 0.99 for transfers using standard ADE techniques. 2 value and an R of approximately 0.98 for manual pipette transfer of the same sheared gDNA sample. 2 FIG. 9 is a plot of a standard curve generated using transfer, showing a high degree of linearity, highlighted by values. Specifically, FIG. 9 shows Cp vs. log[DNA] for all technical replicates of a sheared 8Kb PROMEGA® sample (N=32). ADE transfer precision, shown as percent CV, was less than 2%. This set of experiments serves as a baseline for "expected" result quality. Note that the hand pipetting control results have less linearity than the ADE results, but are still acceptable.

[0035] Experimental data using unprocessed, unsheared, intact native gDNA from a commercial source and utilizing a non-Newtonian calibration. The next step was to test the newly determined parameter space (tone burst and ejection energy regimes) for non-Newtonian viscoelastic fluids utilizing native gDNA from two commercial sources, BIOLINE® and PROMEGA®, with fragment sizes of 40 Kb and 56 Kb, respectively. These samples were manually pipetted into acoustically qualified tubes in preparation for transfer according to the techniques described herein. These samples were analyzed to determine and confirm fragment size using an AATI Fragment Analyzer (AATI, Ames, IA). The experimental results are shown in Figure 10, highlighting that the samples are indeed 41 Kb and 56 Kb in length. Next, non-Newtonian tone burst and higher energy calibrations were applied to the ADE transfer using an Echo550 liquid handler on native gDNA (unspiked). The concentration of each native gDNA sample in the source tube was 200 ng / μL (also confirmed with a NanoDrop instrument (THERMOFISHER), results not shown). Transfer volumes ranged from 2.5 nL to 320 nL (1 droplet up to 128 droplets) to the destination plate, which also served as the source plate for the qPCR assay. To address the reproducibility, accuracy, and precision of each biological sample, 32 technical replicates were transferred for each experimental condition (to demonstrate statistical significance). Additionally, these experiments were repeated on multiple days to confirm the validity of the results. Table 3, Figure 11, and Figure 12 show the Cp values, highlighting the validity of the high reproducibility, high accuracy, and high precision transfer capabilities using this new calibration for non-Newtonian fluids. For all transfers, the number of replicates transferred was 32 except for days 1 and 2 of PROMEGA, where the transfer volume was 5 nL, and day 2 of PROMEGA, where the transfer volume was 2.5 nL. For all other samples, all 32 replicates were transferred. The linearity of the standard curves generated using all transfers was R ≥ 0.99. 2The percent CV for all transfers was less than 5% (and in most cases <3%).

[0036] Figures 10A and 10B show the AATI fragment analyzer traces for the BIOLINE® (40 Kb) sample (Figure 10A, Chart 1002), and the PROMEGA® (56 Kb) gDNA sample (Figure 10B, Chart 1004), highlighting that the samples were indeed the size indicated by the commercial vendor. The qPCR Cp data for these samples is shown in Table 3 below. [Table 3-1] [Table 3-2]

[0037] Figure 11 shows the qPCR data as measured by Cp vs. log[DNA] for biological and technical replicates of native gDNA (N=26-32), and Figure 12 shows the percent CV of Cp vs. log[DNA] for all biological and technical replicates listed in Table 3. High linearity is observed in the R 2 Values ​​>0.99. Furthermore, high precision and reproducibility are demonstrated by the low CV values ​​of replicates at each concentration of the experimental run in FIG.

[0038] Experimental data using native gDNA from unprocessed, unsheared, intact human samples with non-Newtonian calibration Native gDNA from six human samples extracted from whole blood, all with fragment sizes above 15 kKb, with a maximum fragment population of approximately 150 Kb, was then transferred from the acoustically qualified tubes (Figure 13). The concentration of each native gDNA sample in the source tube was determined to be between 100 ng / μL and 175 ng / μL (results confirmed with NanoDrop not shown). Transfer volumes ranged from 1 droplet (2.5 nL) to 128 droplets (320 nL) into the qPCR source wells for loading the reaction. To address precision, for each biological sample, 10 technical replicates were transferred per condition. Given the limited amount of gDNA samples, the experiment was performed only once, but no differences are expected. Table 4, Figure 14, and Figure 15 show the Cp values, again highlighting the high precision, accuracy, and reproducibility of the transfer of human native gDNA samples. For all samples, all 10 replicates were transferred with 100% success rate with no dropouts (0% dropouts). The linearity of the standard curve generated using all transfers was R 2 The percent CV of Cp for all transfers was <2.5%. [Table 4-1] [Table 4-2]

[0039] FIG. 13 shows the AATI fragment analyzer trace 1300 of a human gDNA sample showing large fragment size. The gDNA size was assessed in FIG. 13 and confirmed to be large fragment size for the sample. FIG. 14 shows the qPCR data as measured by Cp vs. Log[DNA] data 1400 for all biological and technical replicates as described in Table 4. The transfer results were compared in R for six different samples of human gDNA. 2Values ​​>0.99, indicating highly reproducible and highly linear transfer. Figure 15 shows the percent CV of Cp vs. log[DNA] data 1500 for biological and technical replicates for all human samples, as described in Table 4. The CV of Cp's for all transfers for biological and technical replicates was ≤3.2%.

[0040] Pre- and Post-Transfer Analysis Genomic DNA sample integrity is a key concern for many biological applications, workflows, especially gDNA stores and repositories. The data highlighted in section confirms that native gDNA transferred by ADE with new tone bursts and acoustic energy ranges, as well as that the transferred gDNA can subsequently be successfully amplified and quality assessed in qPCR assays. To further evaluate the properties of the transferred native gDNA, the post-transfer samples were analyzed to determine and confirm fragment size using the AATI Fragment Analyzer (AATI), and the native gDNA retained its integrity (with minimal shearing). Figure 16A-F, and Table 5 all show the AATI Fragment Analyzer traces of human native gDNA samples commercially obtained from BIOLINE® and PROMEGA® as well as human samples.

[0041] The AATI traces for each of these samples showed either a small enhancement in gDNA size (within the experimental error of the method) or a small downward shift relative to the post-transfer state, suggesting a possible small amount of shearing. However, in all cases, the gDNA after transfer in any of these samples was greater than 20Kb, which was at the limit of the Newtonian tone burst and acoustic energy transfer capabilities. The AATI instrument also has a DNA quantification accuracy of ±25% and a DNA quantification precision of 20%CV. All of the data presented here closely matches these specifications. The data generated here represents large fragment gDNA and is shown below in Table 5, which shows the fragment size (Kb) of either manually pipetted (control) commercial and human native gDNA samples compared to the same samples before and after transfer utilizing the novel acoustic non-Newtonian parameters. [Table 5-1] [Table 5-2]

[0042] Fragment profiles are shown in AATI fragment analyzer traces (FIGS. 16A-F) for manually pipetted (bottom-1603a-1603f), pre-transfer (middle-1602a-1602f), and post-transfer (top-1601a-1601f) traces for each type of gDNA sample selected. FIG. 16A shows the traces for the BIOLINE gDNA sample. FIG. 16B shows the traces for the PROMEGA gDNA sample, and FIGs. 16C-F show the traces for each of the human native gDNA samples, Sample 1 (FIG. 16C), Sample 2 (FIG. 16D), Sample 3 (FIG. 16E), and Sample 4 (FIG. 16F).

[0043] Melt Curve Analysis The success of a qPCR reaction is based not only on DNA amplification but also on melt curve analysis. During a qPCR reaction, as amplicons are produced, the fluorescent SYBR green dye binds to double-stranded DNA (dsDNA) and these complexes fluoresce, generating a fluorescent signal and a characteristic amplification curve. The amplification curve consists of three phases: the initiation phase, the exponential phase, and the plateau phase. The initiation phase is almost always below the level of fluorescence detection. In the exponential phase, copies of the newly synthesized dsDNA are made and thus are represented by an increase in fluorescence as the SYBR dye binds to the dsDNA. Finally, the plateau phase marks the end of the reaction and is also called the stabilization phase.

[0044] Each particular amplicon has a characteristic amplification curve. As amplification occurs, the number of qPCR cycles is recorded, and as a result, in the exponential phase, a threshold value, called the cycle threshold (Ct) or crossing threshold (Cp), is utilized to assess the level at which amplification occurs (see FIG. 17, plot 1702). The amplification curve is plotted as a function of the crossing point (Cp), also known as the cycle threshold (Ct) or cycle number. The lower the cycle number (Ct or Cp), the higher the initial amount of DNA. The higher the cycle number (Ct or Cp), the lower the amount of initial DNA, and therefore more cycles are required as the concentration of DNA increases per cycle. To ensure the specificity of the qPCR reaction, a melt curve analysis was also performed to measure the temperature at which the amplicon dissociates from dsDNA to single-stranded DNA (ssDNA). After amplification, the double-stranded SYBR green-bound amplicon was exposed to an incremental increase in temperature (plot 1704). When the temperature exceeds the melting temperature of the primers, the amplicon dissociates and the SYBR green fluorescent signal decreases. The slope of this curve is then plotted as a function of temperature to generate the melt curve (Figure 17, plot 1706), an analysis used by most researchers in the field.

[0045] In all studies described here, the β-actin amplicon is 266 base pairs (bp) with a melting peak temperature at 88 ± 1 °C. Positive control 8-point 2-fold dilution standard curves ranging from 100 to 0.78 ng / µL demonstrated amplicon Cp / Ct ranging from 20 to 32, respectively.

[0046] The techniques described herein can be applied to a variety of sample types and sample transfer protocols. For example, in many research and clinical laboratories, genomic deoxyribonucleic acid (gDNA) is extracted from both fresh and archived samples, including but not limited to bacteria, viruses, plants, whole blood (WB) and components (plasma, serum), tissues, formalin-fixed paraffin-embedded (FFPE) tissues, cells, saliva (-buccal), urine, and cerebrospinal fluid (CSF).

[0047] The techniques described herein are useful in any DNA technology application requiring high sample integrity (e.g., samples from a biostore or repository) where intact native gDNA is desired to be processed by a liquid handler for downstream applications. Because DNA is a fundamental component of life, applications are widespread and may be used in clinical / diagnostic applications, medical research, medical therapy, pharmaceutical / biotechnology research applications, agricultural applications, and forensics.

[0048] Some embodiments of the present invention enable highly reproducible, highly accurate and precise acoustic droplet ejection of viscoelastic polymer melts and polymer solutions previously inaccessible to ADE. In the past, methodologies to reduce viscosity and reduce elasticity, such as heating and diluting the polymer melt or polymer solution sample with a solvent, have shown only modest levels of success and have involved altering the native sample and potentially damaging the sample (via thermal inactivation or solvent-driven decomposition). Some embodiments of the present invention have the ability to address semiconductor applications, such as the ability to miniaturize the deposition of photoresist or the investigation of polymer samples and polymer melts. Additionally, drug delivery applications are also effective as high doses of "time-released" drugs can be doped into the polymer solution and transported to a receiver to create ultra-dispersed "loaded" drug delivery agents in the form of monodisperse spheres with the highest surface area to volume ratios for the most efficient drug delivery.

[0049] FIG. 18 shows the time evolution of droplet formation for a non-Newtonian liquid exhibiting shear thinning behavior. The fluid contains gDNA from PROMEGA®. Note the long filament formation and long time scale required before droplet release compared to Newtonian fluids with similar viscosity. The present disclosure allows the head of the filament to break into droplets in a robust manner meaning that the volume of these droplets is constant, and the filament returns to the reservoir without further breakup (with the beads on the strand reproducibly shrinking). This was performed using transducers on our Echo550 and Ech 555 instruments, which generate a 2.5 nL drop (higher frequency, shorter wavelength). The droplet size here is estimated to be approximately 2.5 nL. These methods can be applied to any suitable non-Newtonian fluid, particularly standard nucleic acid elution buffers such as, but not limited to, water or TE.

[0050] 18, application of acoustic energy to a fluid surface 1802 induces droplet ejection by generating a leading protrusion 1806 and a mound 1804 that further develops into a filament 1808. In existing methods of ADE, acoustic energy provides momentum for a slug that forces the fluid off the fluid surface, but for non-Newtonian fluids, these methods tend to generate ephemeral protrusions that easily detach or are reabsorbed from the fluid surface 1802. According to embodiments described herein for ejecting droplets of a non-Newtonian fluid, an initial toneburst generates an elongated filament 1808 containing most of the fluid that rises from the fluid surface 1802. Some non-Newtonian fluids also generate a series of accumulations or beads 1810 of fluid along the filament when drawn into the elongated filament 1808, which may consist of one, two, five, ten or more beads, where the size of the beads may be 10, 20, or 50% thicker than the filament diameter during the elongation phase, with 50%, 75%, or 90% of the resulting droplets being less than the filament diameter. When conventional ADE methods are applied to non-Newtonian fluids at low or standard ejection amplitudes relative to Newtonian fluids, the effective elasticity of the filament 1808 tends to prevent droplet ejection. Conversely, when conventional ADE methods are applied at very high amplitudes, the filament 1808 tends to break between the beads 1810, causing unpredictable ejection of multiple smaller droplets. This effect is undesirable not only because of the loss of control over the droplet ejection volume, but also because the sudden separation of the elongated filament 1808 may shear DNA or other contents of the fluid. In contrast, the methods described herein provide clean separation between the droplet 1812 and the remainder 1814 of the elongated filament 1810. In some embodiments, separation of the droplet 1812 can occur when the filament 1810 is very long compared to the droplet diameter. Separation can occur for filament lengths of 2 times or more, 5 times or more, or 10 times or more, or 20 times or more, or 50 times or more, or 100 times or more of the diameter of the ejected droplet, in some embodiments.In some embodiments, during contraction of the filament, beads may bond with each other or coalesce into the retracting remainder 1814, thereby decreasing the number of beads and increasing the size of the merged beads as they retract toward the meniscus. In some embodiments, the volume of the droplet 1812 may be significantly smaller than the volume contained within the filament 1810 upon separation, for example having a diameter of less than 10% of the length of the fluid filament 1810. In some embodiments, the volume contained in the ejected droplet 1812 may be less than 50%, less than 10%, or less than 5% of the volume of the remainder 1814.

[0051] FIG. 19 is a schematic diagram 1900 showing the time evolution of droplet formation of different DNA solutions and different size scales. Here, the fluids were from BIOLINE® and transfer was performed with an Echo525 instrument (lower frequency and larger wavelength than the Echo550 or Echo555 instruments) resulting in a nominal droplet size of 25 nL. As shown in FIG. 19, application of acoustic energy to the fluid surface 1902 performs droplet ejection by creating a mound 1904 that further develops into a leading protrusion 1906 and a filament 1908. An initial tone burst generates an elongated filament 1908 containing most of the fluid that rises from the fluid surface 1902. In response to additional tone bursts, droplet 1912 and remaining portion 1914 of elongated filament 1910 separate from each other without emitting smaller droplets. In some embodiments, droplet 1912 may be significantly smaller than filament 1910, for example having a diameter less than 10% of the length of fluid filament 1910. In some embodiments, the volume contained in the ejected droplet 1912 may be less than 50%, less than 10%, or less than 5% of the volume of the remaining portion 1914.

[0052] According to some embodiments, a toneburst as described herein includes three segments. In one embodiment, to create a 2.5 nL droplet of a non-Newtonian fluid, segment 1 is the longest and of high intensity, followed by a short segment 2 where no acoustic energy is generated, and then a final segment (3) of intermediate duration (here, 18 microseconds). Note that droplet ejection can be achieved through adjustment of the power intensity and length of the various segments. The power levels of both toneburst A and toneburst B require much higher power levels than that of a Newtonian fluid to achieve robust volume transfer and reproducible contraction that returns the filament to the reservoir (avoiding the creation of small satellites), as shown in Figures 20 and 21. Toneburst characteristics are listed in Table 6 below. [Table 6]

[0053] FIG. 20 shows droplets of the same non-Newtonian fluid transferred with tonebursts A (2002) and B (2004) at various acoustic power levels (shown in dB increasing from left to right). At low levels, droplets do not eject from the filament. At high levels, only droplets transfer and have good placement (8 dB and 10 dB, respectively). At higher levels, transfer quality degrades and the filament breaks up into less than a single desired droplet. Any auxiliary droplets or satellites are undesirable. A preferred implementation of tonebursts and powers is one that forms a single droplet of the desired volume and the filament contracts into the reservoir, as shown in the outlines of FIG. 18 and FIG. 19.

[0054] FIG. 21 also shows how transferred volume changes with increasing acoustic power in a chart 2100 comparing ejection volume to ejection power. In some embodiments, a lower slope of the volume vs. power curve is desirable (see, for example, the volume vs. power curves for toneburst A 2102 vs. toneburst B 2104), which reduces the sensitivity to power in transferred droplet volume. Adjustments to the relative amplitude, duration, and frequency content of the tonebursts of each segment can be modified to improve sensitivity characteristics of droplet volume characteristics. FIG. 22 shows a scatter plot 2200 of measured volumes transferred for BIOLINE® fluid for both large (2202) and small droplets (2204) with their associated volumes correlated to various ejection powers and power sensitivities.

[0055] Figures 23-24 show other specific embodiments of the invention, applied to fluids whose non-Newtonian behavior is not anionic and not due to nucleotide polymers such as DNA. One example is for another non-Newtonian fluid class called shear-thinning fluids (Figure 23, image 2300), and the other is for shear-thickening fluids (Figure 24, image 2400). Image 2300 (Figure 23) shows successful droplet transfer for a cationic homopolymer consisting of the amino acid lysine solution, a 25% poly-l-lysine solution (% by volume) in water, which is shear-thinning. Image 2400 (Figure 24) shows successful droplet transfer for a 5% corn starch solution (% by volume) in water, which is a dilatant fluid consisting of a shear-thickening or colloidal suspension. The existence of power windows that work for each fluid type is clearly indicated by 2302 and 2402. However, many other types of fluids, classes of solutes and non-Newtonian classifications can be processed by ADE according to the methods described herein. For example, suitable non-Newtonian fluids that can be transported using the methods of the present disclosure include, but are not limited to, cationic, anionic, nucleoside, and peptidine solutions, suspensions, colloids, dilatants, and other fluids.

[0056] Figures 25-28 show data for yet another DNA-containing fluid, demonstrating the robustness of the method by quantifying DNA transferred from several different wells and measured by qPCR. A layout map of the test plate is shown in Figure 25, which uses both high and low acoustic frequencies to create small and large droplets, respectively, for a range of concentrations of DNA solutions and various numbers of droplets collected in each well. Figures 26 and 27 show that the response is highly repeatable and linear for some power levels, with Figure 26 showing Ct data 2600 for small droplets and Figure 27 showing Ct data 2700 for large droplets. Ejection tone burst amplitudes ranged from about 6 dB (2601, 2701) to about 11 dB (2602, 2702) in 0.5 dB increments. The Y-axis is Ct, which is the number of cycles of the qPCR instrument required to amplify the DNA to a particular threshold level. Thus, a lower Ct corresponds to a starting DNA concentration at an earlier stage. The power levels to achieve linearity in the expected slope versus starting concentration and droplet count are highlighted in the table in Figure 28. For both small droplet size 2802 and large droplet size 2804, the results are repeatable and linear, and are achievable over a wide range of power levels, even with large gDNA (68 Kb) and high gDNA concentrations (105 ng / L).

[0057] 29-31 show exemplary processes for setting droplet ejection parameters and ejecting droplets of a non-Newtonian fluid. Processes 2900, 3000, 3100, and 3200, as well as any other processes described herein, may be performed by any suitable acoustic droplet ejection system as described herein.

[0058] FIG. 29 illustrates a first exemplary process 2900 for setting droplet ejection parameters and ejecting droplets of a non-Newtonian fluid according to a dynamic mound analysis (DMA) technique, whereby toneburst power is gradually increased, according to at least some embodiments. First, a reservoir containing an unknown non-Newtonian fluid can be inserted into an acoustic ejection device (2902). Toneburst power is increased (2904) and then applied to the fluid in the reservoir (2906). At least one, and often many, echo signals are collected and analyzed at each toneburst power (act 2908). These processes are repeated in a do loop (2910) until the maximum possible power is reached. Once the maximum power is reached, the system extracts signal features of the echo signals (2912) for each toneburst setting (e.g., toneburst pattern and length of each toneburst, toneburst amplitude, toneburst frequency, and frequency pattern, etc.). The optimal signal signature is selected (2914) using a pre-trained artificial intelligence or machine learning model. The model correlates signal features extracted based on collected echo signals to known tone burst power settings. The model looks for trends in the echo or optical signal that change over time. Signal features can increase, decrease, or remain constant for successive time delays. At least one echo signal is required to train the model. The system then selects (2916) an optical signal signature according to the methods described above, and can then select signal features and signal signatures for ejecting subsequent droplets.

[0059] FIG. 30 also illustrates a second exemplary process 3000 for setting droplet ejection parameters and ejecting droplets of a non-Newtonian fluid, according to DMA, whereby toneburst power is gradually increased. As described above, a reservoir containing an unknown non-Newtonian fluid can be inserted into the acoustic ejection device (3002). Toneburst power is increased (3004) and then applied to the fluid in the reservoir (3006). Echo signals are collected and analyzed (3008) at successive power steps to determine when a good signal is received (3010), where the signal quality is based on one or more signal parameters, such as, for example, sufficient power, clarity of the return signal, or other parameters. This comparison may include comparing the signal quality features to a predefined threshold. Once a good echo signal is received, the system extracts signal features (3012) as described above, and the signal features can then be fed into a model to address the target / solvent concentration (3014). The model outputs a decision of whether the target / solvent concentration is low or high based on the acquired signal features (3016). For low concentrations, the system may select an optimal signal signature for the solution (3024) and then select a signal feature and signature for ejecting a subsequent droplet (3022) as described above. For high concentrations, the system may take an additional intervention step by examining an optimal segment length (i.e., tone burst length of the tone burst pattern) (3018) and then adjusting tone burst attributes (3020) before selecting an adjusted signature for the signal feature and subsequent droplet ejection.

[0060] FIG. 31 illustrates a third exemplary process 3100 for setting droplet ejection parameters and ejecting droplets of a non-Newtonian fluid according to dynamic break-off analysis (DBA). As described above, a reservoir containing an unknown non-Newtonian fluid can be inserted into the acoustic ejection device (3102). Tone bursts are emitted into the fluid by an ADE system (3104), which listens for echoes (3106), and a frequency filter is applied to the echo signal (3108) to detect if a droplet has been detached (3110). Playback occurs at least once, and often multiple times, during each tone burst repetition. If a droplet has not been detached, the system increases the ejection power (3112) and repeats the process by emitting another tone burst with the adjusted ejection power. Failed or successful droplet detachment can be optically observed during each modulation of the tone burst. Once droplet detachment is detected, the system can set the ejection parameters for subsequent droplets based on the last successful droplet ejection power and "lock-in" the desired parameters (3114).

[0061] FIG. 32 also illustrates a fourth exemplary process 3200 for setting droplet ejection parameters and ejecting droplets of a non-Newtonian fluid, also by DBA. As described above, a reservoir containing an unknown non-Newtonian fluid can be inserted into the acoustic ejection device (3202). A toneburst is emitted into the fluid (3204) by an ADE system, which listens for echoes (3206), and a frequency filter is applied to the echo signal (3208) to detect whether a droplet has detached (3210). Playback occurs at least once, and often multiple times, during each toneburst repetition. If a droplet has not detached, the system modulates the toneburst (3212), for example, by changing one or more of the toneburst segment lengths, and repeats the process by emitting another toneburst using the modulated toneburst. Failed or successful droplet detachment can be optically observed during each modulation of the toneburst. Once droplet detachment is detected, the system can set the ejection parameters of subsequent droplets based on the last successful droplet ejection toneburst characteristics and "lock-in" the desired parameters (3214).

[0062] Any or all of processes 2900, 3000, 3100, 3200 may be used in conjunction with each other or with any of the techniques described in detail above. For example, the system may use both DMA and DBA techniques to determine and store optimized drop ejection characteristics (e.g., tone burst segment length, tone burst ejection power), or may use any suitable combination of these techniques to further optimize drop ejection parameters.

[0063] The embodiments of the systems and methods described herein may also be used to utilize machine learning algorithms (MLAs) to classify, characterize, and subsequently determine the correct dynamic fluid parameter information content to enable acoustic droplet ejection (ADE) of non-Newtonian fluids. This enables the ability to use ADE to transfer samples of non-Newtonian fluids in a highly reproducible, highly accurate, and highly precise manner (such as intact native genomic DNA or viscoelastic polymers in solution) and quantify using standard laboratory techniques such as qPCR, fluorescence-based assays, or NGS for varied sample inputs. The dynamic mound analysis (DMA) described above enables the ability to utilize acoustic droplet ejection (ADE) technology to characterize and classify sample concentrations, fragment sizes, and preparations of non-Newtonian fluids (such as intact native genomic DNA, or viscoelastic polymers in solution). Also described above, dynamic detachment analysis (DBA) enables the ability to utilize ADE techniques to characterize tone burst output volume, amplitude, frequency, or other attributes for droplet detachment of non-Newtonian fluids (such as intact native genomic DNA (gDNA), or viscoelastic polymers in solution). Additional methods enable reproducible, accurate, and precise prediction of droplet volume using dynamic volume analysis (DVA) for non-Newtonian fluids using ADE for downstream applications such as qPCR, fluorescence, NGS, and other genomic applications.

[0064] Each of the three analysis modes mentioned above (DMA, DBA, DVA) is configured to use multiple acoustic reflections in conjunction with machine learning based on a training dataset to obtain fluid parameter information 1) before and 2) after acoustic droplet ejection of non-Newtonian fluids.

[0065] For the class of non-Newtonian fluids (exhibiting viscoelastic properties), the use of impedance-based signatures (and associated power thresholds) does not allow for successful characterization or classification for acoustic droplet ejection. Even with well-understood parameters such as sample concentration, fragment size, or preparation method, the impedance changes are too broad to detect. Although the addition of solutes (e.g., gDNA) to a Newtonian buffer solution does not significantly change the impedance of the fluid and therefore does not greatly affect the signature readout, this addition gives rise to viscoelastic phenomena referred to as non-Newtonian fluid behavior that require significantly different powers and alternative tone burst parameters to allow ADE.

[0066] Non-Newtonian properties can be assessed using one or more sub-ejection energy bursts that perturb the fluid surface and collect one or more acoustic signals reflected from the resulting mounds. For example, dynamic mound analysis (DMA) enables the ability to characterize and classify sample concentrations, fragment sizes, and preparations of non-Newtonian fluids (such as intact native genomic DNA, or viscoelastic polymers in solution) utilizing acoustic droplet ejection (ADE) techniques.

[0067] According to various embodiments, a model can be generated that analyzes acoustic reflections from the fluid surface after being perturbed by a tone burst, using a binary scheme to classify the ADE event as successful or unsuccessful. The results can be interpreted as iteratively adjusting the tone burst until a successful droplet detachment event occurs. This methodology is called Dynamic Detachment Analysis (DBA), which differs from Mound Image Print (MIP) because non-trivial signal processing algorithms are preferred over null space analysis when investigating non-Newtonian fluids in containers, well plates, or reservoirs. Importantly, MIP results predict successful droplet detachment events at 6-11 dB lower power than correctly determined DBA results.

[0068] A surrogate model can be generated that analyzes the droplet volume resulting from a successful ADE event. The model is trained and processed on acoustic reflections from the fluid surface before, during, and after the droplet ejection event. This dynamic volume analysis (DVA) achieves accurate droplet volume predictions for any droplet of non-Newtonian fluid (e.g., DNA with different concentrations, fragment sizes, and preparations) created using any tone burst.

[0069] Human genomic DNA (gDNA) samples are utilized as a surrogate for non-Newtonian fluids. gDNA samples can vary in concentration, fragment size, and preparation, factors that are difficult to parameterize. Therefore, a training set incorporated into a machine learning algorithm (MLA) can be utilized to enable ADE that is highly reproducible, highly accurate, and highly precise.

[0070] 33-35 show exemplary processes for data collection and processing to characterize a non-Newtonian fluid, determine suitable toneburst parameters for acoustic droplet ejection, and characterize the droplet volume of an ejected droplet of a non-Newtonian fluid. Processes 3300, 3400, 3500, and any other processes described herein, may be performed by any suitable acoustic droplet ejection system as described herein.

[0071] Dynamic Mound Analysis FIG. 33 illustrates an exemplary process 3300 for using dynamic mound analysis to characterize non-Newtonian fluids and fix the identity of the fluid. According to various embodiments, a fluid with unknown properties is contained within a fluid reservoir (3302). Identification or characterization of the fluid is performed using dynamic mound analysis (DMA). First, a series of acoustic signals are generated and focused at the surface of the fluid (Act 3304). These signals perturb the surface but keep the energy low enough to avoid breaking off any droplets of the fluid from the bulk. After each perturbation, a series of interrogation pings or interrogation tone bursts are emitted and focused at the surface of the fluid (Act 3306), after which acoustic reflections (or echo signals) are collected from the fluid surface (3308) until a maximum wait time is reached (3310). The collection of these reflections is processed (3312) to determine the non-Newtonian behavior of the fluid.

[0072] Analysis of multiple unknown fluids can provide results that can be relative and are a ranking of the degree of non-Newtonian fluid parameters. This qualitative ranking is simple and is achieved by inspection of the signal collection. Quantitative classification of these fluids is then achieved (3314) using a convolutional neural network (CNN) that is trained and validated using samples of known concentration, fragment size, and preparation method. This is the application of a general technique for non-trivial classification to generate a fluid classification model that is utilized to perform ADE with reproducibility, accuracy, and precision. The data set consists of reflected acoustic signals collected from a fluid mound generated at various sub-ejection power levels. The collection of reflected time domain signals can be used unfiltered to train the model. Alternatively, the collection of reflections may be processed using a fast Fourier transform (FFT) before training the model.

[0073] According to one CNN model trained on FFT signals from 15 unique samples, 99% of samples are correctly characterized, enabling successful gDNA transfer by ADE. Samples are unique based on a combination of gDNA concentration, fragment size, and preparation method. These unique characteristics are summarized in Table 1 below. [Table 7]

[0074] Dynamic Churn Analysis (DBA) FIG. 34 illustrates an exemplary process 3400 for achieving acoustic droplet ejection and locking in tone burst parameters, i.e., using dynamic breakaway analysis (DBA) to select the correct energy level for ADE for non-Newtonian fluids (such as, but not limited to, gDNA). According to various embodiments, the characterized non-Newtonian fluid is contained within a fluid reservoir (3402). A high energy acoustic signal, or tone burst, is focused and targeted (3404) to the fluid contained within the reservoir. A series of interrogating tone bursts is applied (3406) to the fluid within the reservoir, after which acoustic reflection or echo signals are collected (3408) until a maximum wait time is reached (3410). The success or failure of the ejection tone burst may be confirmed (3410) by optical methods, such as scanning an area on the reservoir for the passage of droplets, acoustic methods, such as based on analysis of the collected echo signals, or other suitable methods.

[0075] The outcome of each droplet ejection attempt may be classified as successful, unsuccessful, or inconsistent. Perturbation of the fluid surface and the absence of droplet break-off or detachment are defined as failed attempts. The consistency classification describes droplet break-off events that occur intermittently or produce inconsistent droplet volumes that are not highly repeatable, accurate, or precise. Finally, successful events are defined as droplet break-off events that transfer droplets of non-Newtonian fluid from the source reservoir with high repeatability, accuracy, and precision. Iterative increases in toneburst energy (3412) and DBA analysis may be performed until a successful ADE event occurs (3414). At this point, the parameters (i.e., toneburst pattern and energy) may be "locked in" to perform subsequent ejections (3416). If the acoustic reflection after the toneburst event registers as a failed event, the energy level may be increased and subsequent acoustic reflections may be analyzed. For optimal performance, the ejection energy may be increased in stages until a successful event is "locked in."

[0076] Dynamic Volume Analysis (DVA) FIG. 35 illustrates an exemplary process for using dynamic volume analysis to characterize the volume of droplets generated by acoustic droplet ejection and lock in the droplet volume. According to various embodiments, a non-Newtonian fluid is contained within a fluid reservoir (3502). Acoustic droplet ejection is triggered (3504) by aiming a focused acoustic tone burst at a fluid surface within the reservoir. The dynamic fluid surface is then informed (3506) using a series of interrogation pings until a maximum wait time is reached (3510). The acoustic reflection or echo signal is then analyzed to predict the droplet volume (3512) and can be compared to measured droplet volume data to characterize the volume of the formed droplet (3512). The process can be performed iteratively to lock in the droplet volume (3516) by varying parameters of the model.

[0077] One method of determining droplet volume is to obtain a series of acoustic reflections for each acoustic droplet ejection. The raw time domain signal may be processed through a Hilbert Transform, with further processing, if necessary, by thresholding and blanking pixels below a critical grayscale value to simplify feature extraction and attenuate noise. Key features that may be extracted from the acoustic reflection images include, for example, the center of mass (derived from the density and amplitude of the signal amplitude), leading edge of each filtered reflection, and peak features. These extracted features are combined with attributes of the tone burst used to create the droplet, and the set of features is used to train a model to predict droplet volume. Training and validating the neural network model results in a characterization of the droplet volume with high accuracy, precision, and repeatability, as shown in FIG. 9. Each point has a known or expected droplet volume associated with its characteristic DVA signal feature.

[0078] Genomic validation to assess sample integrity and unbiased transfer To quantitatively assess gDNA transfer, non-DMA calibration and control experiments using DMA for ADE of gDNA, quantitative polymerase chain reaction (qPCR) was utilized to confirm and detect the expression of the housekeeping gene beta-actin (~actin). qPCR assays were performed using the following: ~-actin forward primer: AGC CAT GT A CGT TGC TAT CC; ~-actin reverse primer: CGT AGC ACA GCT TCT CCT TAA T, (IDT). All primers were at a final concentration of 1 μM. qPCR reaction assays were performed on a THERMOPISHER QuantStudio 6 Flex Real-Time PCR System using PowerUp™ SYBR® Master Mix and a THERMOPISHER 384-Well Clear Optical Plate.

[0079] The qPCR program was used as follows: step 1, 50°C for 2 min, 95°C for 2 min, step 2, 95°C for 15 s, 60°C for 1 min, collect (40 cycles), step 3, 95°C for 15 s, 60°C for 1 min, and 95°C for 15 s. Human native gDNA was obtained from BIOLINE (45Kb), NOVAGEN (68Kb), and PROMEGA (55Kb). Each vendor represented a different preparation method, and all samples were used as received or diluted to the final desired concentration in Tris-EDTA (TE) buffer pH 8.0. Six-point two-fold standard curves were generated by acoustically transferring 1, 2, 4, 8, 16, and 32 droplets of gDNA solution. Eight (n=8) technical replicates were performed for each concentration of gDNA.

[0080] For comparison, a control set of qPCR results for the transfer of gDNA solution using non-DMA calibration was obtained. The CV of non-DMA calibration shows high variance, with many points having more than 5% CV. Additionally, for BIOLINE at 150ng / μL and 200ng / μL, NOVAGEN at 80 and 105ng / μL, and PROMEGA at 100, 150 and 200ng / μL, the R'2 value is <0.51. This is not reproducible, accurate, or precise. Additionally, non-DMA calibration caused many transfer failures or no amplification in target wells.

[0081] In contrast, transfers obtained using DMA calibration robustly and reproducibly transferred gDNA solutions. For each preparation method, CVs were generally <4% with R / 2 values ​​>0.9 for BIOLINE and NOVAGEN. For PROMEGA at 100, 150, and 200 ng / uL, R / 2 values ​​are much higher at >0.8 compared to <0.51 using non-DMA calibration.

[0082] Volume validation of DV A using fluorescent and qPCR assays The DVA model was also trained using droplet volume data collected using a fluorescence-based assay to confirm volume accuracy and precision. Solutions with varying concentrations of gDNA were prepared in 0.0375 mM sodium fluorescein. The solutions were transferred to the assay plate using a non-Newtonian ADE. The assay plate (1536LDV, LABCYTE) was pre-filled with a solution of 10 mM NaOH as a quench solution. A 6-point 2-fold standard curve was generated using bulk dilutions of the 0.0375 mM solution by pipetting 5 μL into the assay plate. The plates were centrifuged (3,000 rpm), incubated at room temperature for 5 minutes, and read on a fluorescence reader (BMG PHERASTAR).

[0083] Correlations were generated between droplet volumes predicted by DVA and those predicted using the fluorescence-based assay. Perfect correlation has an R / \2=1.0. In these cases, using various tone burst frequencies, the regression slopes showed high correlation with R^2 values ​​exceeding 0.98. DVA performance was also successfully validated using a qPCR assay, with R / \2 values ​​exceeding 0.95.

[0084] Experimental data was obtained using untreated, unfragmented, intact native gDNA from a commercial source, and non-Newtonian calibration was utilized with machine learning algorithms for NGS. To quantitatively assess gDNA transfer using the acoustic parameters described above, we then utilized next-generation library preparation (NGP) and next-generation sequencing (NGS) to confirm that no preferential transfer (e.g., size or single-stranded DNA vs. double-stranded DNA) or damage (e.g., unintentional gDNA shearing) had occurred to DNA sequences that maintained sample integrity.

[0085] NGS was performed using the MiSeq Instrument (Illumina) with the NEXTERA™ DNA Flex Kit (Illumina) for NGP. Data analysis was performed using Illumina's BaseSpace FastQC and BW A Aligner Sequencing Tools. Highly enriched (>100ng / μL), large fragment (>20Kb) gDNA, commercially obtained from BIOLINE and MILLIPORE SIGMA (formerly NOVAGEN), was acoustically or pipetted into 384-destination plates for library preparation (control). A total of 140ng of each gDNA sample was transferred in triplicate technical replicates. All library preparation protocols were miniaturized 3-fold to allow acoustic transfer into 384-well plates with all processes, such as tagmentation, post-tagmentation cleanup and amplification, performed on a THERMOPISHER QuantStudio™ 6 Flex Real-Time PCR System adapted to be used as the end-point PCR instrument. gDNA sample information is shown in Table 8 below. [Table 8]

[0086] The libraries were sequenced on the MI SEQ® Reagent Kit v3 (150 cycles). Even intensity was detected across the runs for each nucleotide, indicating uniform detection of all four bases. In addition, the resulting heat map of QScores showed that the base calling accuracy was greater than 91.5%, indicating that the probability of an incorrect base call was 1 in 1,000. The sequence data quality per base for acoustic and pipette transfer was consistent and of high quality, indicating that the sequence content per base for acoustic and pipette transfer did not differ between the four different bases of the sequence run.

[0087] Tables 9A-D below show the sequencing parameters for both gDNA samples across multiple technical replicates. Properly paired reads were >98% for all samples, indicating that both mates of the reads were in the proper orientation and had a specific insert size in alignment with the genome. For all samples, >99% of the insert sequences aligned with the reference genome, indicating that the libraries generated from acoustically transferred or pipette transfer were comparable and no bias ADE or damage to the gDNA occurred. [Table 9A] [Table 9B-1] [Table 9B-2] [Table 9C] [Table 9D]

[0088] The various computational methods discussed above may be implemented in conjunction with or using a computer or other processor having hardware, software, and / or firmware. The various method steps may be implemented by modules, which may include any of a wide variety of digital and / or analog data processing hardware and / or software arranged to implement the method steps described herein. The modules comprise data processing hardware adapted to implement one or more of these steps, as appropriate, by having appropriate machine programming code associated therewith, and modules for two or more steps (or parts of two or more steps) may be integrated into a single processor board or separated into different processor boards in any of a wide variety of integrated and / or distributed processing architectures. These methods and systems often employ tangible media that embody machine-readable code having instructions for implementing the method steps described above. Suitable tangible media may include memory (including volatile and / or non-volatile memory), storage media (such as magnetic recording on floppy disks, hard disks, tapes, etc., optical memory such as CDs, CD-R / Ws, CD-ROMs, DVDs, or any other digital or analog storage media, etc.), and the like.

[0089] The features illustrated herein are presented by way of example and for the sole purpose of illustrative discussion of preferred embodiments of the present disclosure, where they provide what is believed to be the most useful and readily understood explanation of the principles and conceptual aspects of various embodiments of the present invention. In this regard, no attempt has been made to show structural details of the invention in more detail than is necessary for a fundamental understanding of the invention, and the description is provided by way of figures and / or examples that will make apparent to those skilled in the art how some forms of the invention may be embodied in practice.

[0090] The following definitions and explanations are meant and intended to control in any future constructions unless clearly and unambiguously amended in the examples below or unless application of the meaning renders any construction meaningless or essentially meaningless. If the construction of a term renders it meaningless or essentially meaningless, the definition should be made according to a dictionary known to those skilled in the art, such as Webster's Dictionary, 3rd Edition, or the Oxford Dictionary of Biochemistry and Molecular Biology (Ed. Anthony Smith, Oxford University Press, Oxford, 2004).

[0091] Unless the context clearly requires otherwise, throughout the description and claims, words such as "comprise," "comprising," and the like are to be construed in their inclusive sense, i.e., "including, but not limited to," as opposed to their exclusive or exhaustive sense. Words using the singular or plural number also include the plural and singular number, respectively. Additionally, when used in this application, the words "herein," "above," and "below," and words of similar import, shall refer to this application as a whole and not to any particular portions of this application.

[0092] The description of the embodiments of the present disclosure is not intended to be exhaustive or to limit the disclosure to the precise form disclosed. While specific embodiments and examples of the present disclosure are described herein for illustrative purposes, various equivalent modifications are possible within the scope of the present disclosure, as those skilled in the relevant art will recognize.

[0093] All references, including patent applications (including patents, patent applications, and patent publications), scientific journals, books, articles, technical literature, and other publications and materials discussed in this application are incorporated by reference in their entirety for all purposes.

[0094] Aspects of the disclosure can be modified, as necessary, to employ the systems, functions, and concepts of the above references and applications in order to provide still further embodiments of the disclosure. These and other changes can be made to the disclosure in light of the detailed description.

[0095] Specific elements of any of the foregoing embodiments may be combined with or substituted for elements of other embodiments. Additionally, although advantages associated with certain embodiments of the present disclosure have been described in the context of those embodiments, other embodiments may and do not necessarily exhibit such advantages to fall within the scope of the present disclosure.

[0096] Although the above provides a complete and thorough disclosure of exemplary embodiments of the present invention, various modifications, alternative configurations, and equivalents may be adopted as necessary. As a result, although the embodiments have been described in some detail by way of example and for clarity of understanding, various modifications, changes, and adaptations will be apparent to those skilled in the art. Therefore, the above description and illustrations should not be construed as limiting the present invention, which may be defined by the appended claims.

[0097] Other variations are within the spirit of the present disclosure. Thus, while the disclosed technology is susceptible to various modifications and alternative configurations, certain illustrated embodiments of the disclosed technology are shown in the drawings and described in detail above. However, it is understood that there is no intention to limit the disclosure to the specific form(s) disclosed, but on the contrary, the intention is to cover all modifications, alternative configurations, and equivalents that fall within the spirit and scope of the present disclosure as defined in the appended claims.

[0098] Use of the terms "a," "an," and "the," and similar referents in the context of describing the disclosed embodiments (especially in the context of the claims that follow) should be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms "comprising," "having," "including," and "containing" should be construed as open-ended terms (i.e., meaning "including, but not limited to"), unless otherwise stated. The term "connected" should be construed as partially or wholly contained within, attached to, or joined together, even if there is something intervening. Recitations of ranges of values ​​herein are intended merely to serve as a shorthand method of referring individually to each separate value falling within the range, and each separate value is incorporated herein as if it were individually recited herein, unless otherwise indicated herein. All methods described herein may be carried out in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. Any and all examples provided herein, or the use of exemplary language (e.g., "such as"), are intended merely to better illustrate the embodiments of the present disclosure, and do not impose limitations on the scope of the present disclosure unless otherwise claimed. No language in this specification should be construed as indicating any non-claimed element as essential to the practice of the present disclosure.

[0099] Disjunctive language, such as the phrase "at least one of X, Y, or Z," is intended to be understood within the context in which it is generally used to indicate that an item, term, etc. may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and / or Z), unless specifically stated otherwise. Thus, such disjunctive language is generally not intended, and should not imply, that a particular embodiment requires at least one of X, at least one of Y, or at least one of Z to be present, respectively.

[0100] Preferred embodiments of the present disclosure are described herein, including the best mode for the inventors to carry out the disclosure. Variations of these preferred embodiments may become apparent to those skilled in the art upon reading the foregoing description. The inventors expect that those skilled in the art will adopt such variations as appropriate, and the inventors intend to practice the present disclosure in ways other than as specifically described herein. Accordingly, this disclosure includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the present disclosure unless otherwise indicated herein or otherwise clearly contradicted by context.

[0101] All references cited in this specification, including publications, patent applications, and patents, are herein incorporated by reference to the same extent as if each reference was individually and specifically indicated to be incorporated by reference and was set forth in its entirety herein.

[0102] In the following, further examples are described to facilitate understanding of the invention.

[0103] Example 1. A method for ejecting droplets from a reservoir containing a non-Newtonian fluid by an acoustic droplet ejector, the method comprising: applying focused acoustic energy in a first pattern including first toneburst segments to a fluid surface of the non-Newtonian fluid to generate a leading protrusion and a filament from the fluid surface without separating the filament from the leading protrusion or the fluid surface; and applying focused acoustic energy to the leading protrusion in a second pattern including second and third toneburst segments to eject droplets by separating the leading protrusion from the filament to form droplets.

[0104] Example 2. the first toneburst segment has an amplitude and duration sufficient to increase a leading protrusion and filament from the fluid surface; The second pattern includes at least two toneburst segments, including a second toneburst segment and a third toneburst segment configured to detach a droplet from the filament. separate 2. The method of example 1, comprising:

[0105] Example 3. The method of example 2, wherein the first toneburst segment has a longer duration than each one of the second and third toneburst segments, and the third segment has a longer duration than the second segment.

[0106] Example 4. The method of any one of Examples 1-3, wherein the filament includes at least one bead of fluid, and the second pattern of focused acoustic energy prevents the at least one bead of fluid from breaking up into one or more additional droplets.

[0107] Example 5. The method of any one of Examples 1-3, wherein the droplets have a diameter that is less than 70%, less than 30%, or less than 10% of the length of the fluid filament.

[0108] Example 6. The method of any one of Examples 1-3, wherein the droplet has a droplet volume that is less than 80% of the total volume of the remainder of the fluid filament, preferably less than 10%, preferably less than 5%.

[0109] Example 7. The method of any one of Examples 1-3, wherein the amplitude of the focused acoustic energy exceeds the displacement amplitude associated with ejecting a displacement drop from a Newtonian fluid.

[0110] Example 8. The method of any one of Examples 1-3, wherein the amplitude of the toneburst varies between the first and second toneburst segments or between the second and third toneburst segments.

[0111] Example 9. the filaments of the non-Newtonian fluid generated from the reservoir by the focused acoustic energy include strands containing 0, 1, 2, or >2 beads of the non-Newtonian fluid; The method of any one of Examples 1-3, wherein a second pattern of focused acoustic energy induces droplet detachment from the chain.

[0112] Example 10. The method of Example 9, wherein the filament comprises 5, 10, or <10 beads of a non-Newtonian fluid.

[0113] Example 11. The method of Example 9, wherein the bead of non-Newtonian fluid has a thickness that exceeds the diameter of the filament by 10%, 20%, 50%, or <50% of the diameter of the filament.

[0114] Example 12. The method of Example 9, wherein the beads of the non-Newtonian fluid have a thickness that is no greater than 50%, 75%, or 90% of the resulting droplet diameter.

[0115] Example 13. The method of example 9, wherein the second pattern of focused acoustic energy is configured to effect droplet ejection without generating satellite droplets.

[0116] Example 14. The method of Example 9, wherein the beads on the chain coalesce with each other with the fluid in the reservoir before or after droplet detachment.

[0117] Example 15. The method of Example 9, further comprising optically or electronically monitoring the filament to assess whether droplet detachment was successful or unsuccessful.

[0118] Example 16. The method of any one of Examples 1-3, further comprising predicting droplet detachment based on an optical or electrical signal from a fluid mound formed in the reservoir.

[0119] Example 17. The method of any one of Examples 1 to 3, wherein the non-Newtonian fluid comprises a solution containing genetic material.

[0120] Example 18. applying a series of calibration tone bursts to a fluid surface configured to dispense a volume of fluid from the fluid surface; detecting the formation of a filament in the amount of fluid ejected from the fluid surface or detecting the ejection of a calibration droplet from the fluid surface; The method of any one of Examples 1-3, further comprising: setting one of a power, a wavelength, or a duration of one or more tone bursts of the first pattern of focused acoustic energy or the second pattern of focused acoustic energy based on the detection of the filament or the ejection of the calibration droplet.

[0121] Example 19. The method of any one of Examples 1-3, wherein the second pattern of focused acoustic energy is configured to result in droplet ejection without generating satellite droplets.

[0122] Example 20. The method of any one of Examples 1-3, wherein the second pattern of focused acoustic energy is configured to suppress formation of satellite droplets.

[0123] Example 21. A droplet ejection system configured to eject droplets from a non-Newtonian fluid in a reservoir, the system comprising: 1. An acoustic ejector, comprising: applying focused acoustic energy to a fluid surface of the non-Newtonian fluid in a first pattern to generate leading protrusions and filaments from the fluid surface without separating the filaments from the leading protrusions or the fluid surface; and an acoustic ejector configured to apply focused acoustic energy in a second pattern to the leading protrusion to separate the leading protrusion from the filament without ejecting satellite droplets such that the filament is drawn into the fluid surface; A droplet ejection system comprising: a processor and a memory device storing executable instructions that, when executed by the processor, cause an acoustic ejector to apply focused acoustic energy in a first pattern to a fluid surface of a non-Newtonian fluid and to apply focused acoustic energy in a second pattern to a precursor leaf.

[0124] Example 22. A method of setting parameters of a toneburst pattern for droplet ejection from a non-Newtonian fluid, the method comprising: applying a series of calibration tone bursts to a fluid surface configured to dispense a volume of fluid from the fluid surface; acquiring a series of echo signals corresponding to a series of calibration tone bursts; setting one of a power, a wavelength, or a duration of one or more toneburst segments of a toneburst pattern of focused acoustic energy configured to eject a droplet from a reservoir containing the non-Newtonian fluid based on the series of collected echo signals, the toneburst pattern comprising at least: 1. separate tone burst segments, Second separate tone burst segments, and Third separateand setting a first tone burst segment having a longer duration than the second and third segments, the third segment having a longer duration than the second segment.

[0125] Example 23. determining a minimum toneburst power corresponding to droplet ejection based on the series of collected echo signals; 23. The method of example 22, further comprising: setting one of a power, a wavelength, or a duration based at least in part on a minimum tone burst power.

[0126] Example 24. 23. The method of example 22, further comprising ejecting droplets from the reservoir by applying focused acoustic energy to the reservoir according to a toneburst pattern.

[0127] Example 25. 25. The method of example 24, further comprising detecting the ejection of the droplet via an optical sensor.

[0128] Example 26. repeatedly varying one of a power, a wavelength, or a duration of one or more tone burst segments until a drop ejection is detected; 26. The method of example 24 or example 25, further comprising: setting the power, wavelength, or duration to one or more values ​​corresponding to the detected droplet ejection.

[0129] Example 27. The method of Example 24 or Example 25, further comprising predicting a droplet volume associated with the ejected droplet by processing the sequence of echo signals with a convolutional neural network trained based on a comparison data set including a plurality of reflected acoustic signals corresponding to a respective plurality of non-Newtonian fluids having known properties.

[0130] Example 28. determining an actual droplet volume associated with the ejected droplet; Comparing the actual droplet volume to the predicted droplet volume; 28. The method of example 27, further comprising modifying the convolutional neural network based on the comparison.

[0131] Example 29. determining a droplet volume associated with the ejected droplet; Comparing the droplet volume to a predicted droplet volume; The method of example 24 or example 25, further comprising adjusting the power, wavelength, or duration based on the comparison.

[0132] Example 30. 26. The method of any one of Examples 22-25, further comprising determining a property of the non-Newtonian fluid based on the series of collected echo signals.

[0133] Example 31. comparing the series of echo signals with stored echo data comprising a collection of previously received echoes correlated with a known collection of non-Newtonian fluids; 26. The method of any one of Examples 22-25, further comprising: setting one of a power, a wavelength, or a duration based at least in part on the comparison.

[0134] Example 32. 26. The method of any one of Examples 22-25, further comprising determining a characteristic of the non-Newtonian fluid by processing one or more echo signals with a convolutional neural network trained based on a comparison data set including a plurality of reflected acoustic signals corresponding to a respective plurality of non-Newtonian fluids having known characteristics.

[0135] Example 33. A method for ejecting droplets containing a non-Newtonian fluid from a fluid reservoir, the method comprising: applying a toneburst pattern of focused acoustic energy to a fluid reservoir containing a non-Newtonian fluid, wherein application of the toneburst pattern to the fluid reservoir ejects droplets of the non-Newtonian fluid from the fluid reservoir, the toneburst pattern comprising: 1. separate tone burst segments, 1. separate The second tone burst segment is applied following the separate tone burst segments, and Second separate The third tone burst segment is applied following the separate The method includes applying a tone burst segment of

[0136] Example 34. The method of example 33, wherein the first tone burst segment has a longer duration than the second and third segments, and the third segment has a longer duration than the second segment.

[0137] Example 35. 1. separate increasing leading protrusions and filaments from the fluid surface of the non-Newtonian fluid by a tone burst of Second and third separate and effecting droplet ejection by detaching a leading projection from the filament via a tone burst of at least one of the first and second electrodes.

[0138] Example 36. The method of example 35, wherein the filament includes at least one bead of fluid, and the toneburst pattern prevents the at least one bead of fluid from breaking up into one or more additional droplets.

[0139] Example 37. The method of example 35, wherein the filament of non-Newtonian fluid generated from the reservoir by focused acoustic energy comprises a strand including one or more beads of non-Newtonian fluid.

[0140] Example 38. The method of Example 37, wherein the toneburst pattern induces droplet detachment from the chain.

[0141] Example 39. The method of example 37, wherein the filament comprises 1, 2, or >2 beads of a non-Newtonian fluid.

[0142] Example 40. The method of example 37, wherein the filament comprises 5, 10, or >10 beads of a non-Newtonian fluid.

[0143] Example 41. The method of Example 37, wherein the bead of non-Newtonian fluid has a thickness that exceeds the diameter of the filament by 10%, 20%, 50%, or <50% of the diameter of the filament.

[0144] Example 42. The method of Example 37, wherein the beads of the non-Newtonian fluid have a thickness that is no greater than 50%, 75%, or 90% of the resulting droplet diameter.

[0145] Example 43. The method of example 37, wherein the beads on the chain coalesce with each other with the fluid in the reservoir before or after droplet detachment.

[0146] Example 44. The method of example 37, wherein the droplet volume of the ejected droplet is less than 80%, less than 10%, or less than 5% of the remaining volume of the filament during droplet ejection.

[0147] Example 45. The method of example 37, wherein droplet ejection occurs when the filament has a length of at least 5 droplet diameters, at least 10 droplet diameters, at least 20 droplet diameters, at least 50 droplet diameters, or at least 100 droplet diameters.

[0148] Example 46. The method of example 37, further comprising optically or electronically monitoring the filament to assess whether droplet detachment was successful or unsuccessful.

[0149] Example 47 The method of Example 33 or Example 34, wherein the droplets have a diameter that is less than 70%, less than 30%, or less than 10% of the length of the filament.

[0150] Example 48 The method of example 33 or example 34, wherein the droplet has a droplet volume that is less than 50% of the total volume of the remainder of the fluid filament, preferably less than 10%, preferably less than 5%.

[0151] Example 49. The method of example 33 or example 34, wherein the amplitude of the tone burst exceeds a displacement amplitude associated with ejecting a displacement drop from a Newtonian fluid.

[0152] Example 50. The method of example 33 or 34, wherein the amplitude of the toneburst varies between the first and second toneburst segments or between the second and third toneburst segments.

[0153] Example 51. The method of example 33 or example 34, further comprising predicting droplet detachment based on an optical or electrical signal from a fluid mound formed in the reservoir.

[0154] Example 52. The method of example 33 or example 34, wherein the non-Newtonian fluid comprises a solution containing genetic material.

[0155] Example 53. applying a series of calibration tone bursts to a fluid surface configured to dispense a volume of fluid from the fluid surface; detecting the formation of a filament in the volume of fluid ejected from the fluid surface; 35. The method of example 33 or example 34, further comprising: setting one of a power, a wavelength, or a duration of one or more toneburst segments of the toneburst pattern based on the detection of the filament.

[0156] Example 54. A droplet ejection system configured to eject droplets from a non-Newtonian fluid in a reservoir, the system comprising: an acoustic ejector configured to apply a toneburst pattern of focused acoustic energy to the reservoir such that the focused acoustic energy ejects droplets of the non-Newtonian fluid from the reservoir; a processor and a memory device storing executable instructions that, when executed by the processor, cause the acoustic ejector to apply focused acoustic energy in a pattern, the pattern comprising: 1. separate tone burst segments, 1. separate The second tone burst segment is applied following the separate tone burst segments, and Second separate The third tone burst segment is applied following the separate A droplet ejection system comprising: The present invention provides, for example, the following items. (Item 1) 1. A method for ejecting droplets from a reservoir containing a non-Newtonian fluid by an acoustic droplet ejector, the method comprising: applying focused acoustic energy to a fluid surface of the non-Newtonian fluid in a first pattern including first toneburst segments to generate the leading protrusion and the filament from the fluid surface without separating the filament from the leading protrusion or the fluid surface; and applying focused acoustic energy to the leading protrusion in a second pattern including second and third toneburst segments to eject a droplet by separating the leading protrusion from the filament to form the droplet. (Item 2) the first toneburst segment having an amplitude and duration sufficient to project the leading protrusion and filament from the fluid surface; The second pattern includes at least two toneburst segments configured to detach the droplet from the filament and a third toneburst segment. separate 2. The method of claim 1, further comprising: (Item 3) 4. The method of claim 2, wherein the first toneburst segment has a longer duration than each one of the second and third toneburst segments, and the third toneburst segment has a longer duration than the second toneburst segment. 4. The method of any one of claims 1 to 3, wherein the filament comprises at least one bead of fluid, and the second pattern of focused acoustic energy prevents the at least one bead of fluid from breaking up into one or more additional droplets. (Item 5) 4. The method of any one of items 1 to 3, wherein the droplets have a diameter that is less than 70%, less than 30%, or less than 10% of the length of the filament, or the droplets have a droplet volume that is less than 80%, less than 10%, or less than 5% of the total volume of the remainder of the filament. (Item 6) 4. The method of any one of claims 1 to 3, wherein the second pattern of focused acoustic energy is configured to result in droplet ejection without generating satellite droplets. (Item 7) 4. The method of any one of claims 1 to 3, wherein the amplitude of the focused acoustic energy exceeds a displacement amplitude associated with ejecting a displacement drop from a Newtonian fluid. (Item 8) 4. The method of any one of claims 1 to 3, wherein an amplitude of the focused acoustic energy is varied between the first and second toneburst segments or between the second and third toneburst segments. (Item 9) the filament of the non-Newtonian fluid produced from the reservoir by the focused acoustic energy comprises a strand containing 0, 1, 2, or >2 beads of the non-Newtonian fluid; 4. The method of any one of claims 1 to 3, wherein the second pattern of focused acoustic energy induces droplets detaching from the chains. (Item 10) 10. The method of claim 9, wherein the beads on the chain coalesce with each other or with the non-Newtonian fluid in the reservoir before or after droplet breakoff. (Item 11) 10. The method of claim 9, further comprising optically or electronically monitoring the filament to assess whether the droplet detachment was successful or unsuccessful. (Item 12) 4. The method of any one of claims 1 to 3, further comprising predicting droplet detachment based on an optical or electrical signal from a fluid mound formed in the reservoir. (Item 13) 4. The method according to any one of items 1 to 3, wherein the non-Newtonian fluid comprises a solution containing genetic material. (Item 14) applying a series of calibration tone bursts to the fluid surface configured to eject a volume of fluid from the fluid surface; detecting the formation of a filament in the volume of fluid ejected from the fluid surface or detecting the ejection of a calibration droplet from the fluid surface; 4. The method of any one of claims 1 to 3, further comprising setting one of a power, a wavelength, or a duration of one or more tone bursts of the first pattern of focused acoustic energy or the second pattern of focused acoustic energy based on the detection of the filament or the ejection of the calibration droplet. (Item 15) 1. A droplet ejection system configured to eject droplets from a non-Newtonian fluid in a reservoir, the system comprising: 1. An acoustic ejector, comprising: applying focused acoustic energy to a fluid surface of the non-Newtonian fluid in a first pattern to generate the leading protrusion and the filament from the fluid surface without separating the filament from the leading protrusion or the fluid surface; and an acoustic ejector configured to apply focused acoustic energy in a second pattern to the leading protrusion to separate the leading protrusion from the filament without ejecting satellite droplets such that the filament is drawn into the fluid surface; and A droplet ejection system comprising: a processor and a memory device storing executable instructions that, when executed by a processor, cause the acoustic ejector to apply the focused acoustic energy in the first pattern to a fluid surface of the non-Newtonian fluid and to apply focused acoustic energy in the second pattern to the precursor leaf. (Item 16) 1. A method for setting parameters of a toneburst pattern for droplet ejection from a non-Newtonian fluid, the method comprising: applying a series of calibration tone bursts to the fluid surface configured to eject a volume of fluid from the fluid surface; acquiring a series of echo signals corresponding to said series of calibration tone bursts; setting one of a power, a wavelength, or a duration of one or more toneburst segments of a toneburst pattern of focused acoustic energy configured to eject droplets from a reservoir containing the non-Newtonian fluid based on the series of collected echo signals, the toneburst pattern comprising at least: 1. separate tone burst segments, Second separate tone burst segments, and Third separateand setting a first toneburst segment having a longer duration than the second and third toneburst segments, the third toneburst segment having a longer duration than the second toneburst segment. (Item 17) determining a minimum toneburst power corresponding to droplet ejection based on the series of collected echo signals; 17. The method of claim 16, further comprising: setting the one of power, wavelength, or duration based at least in part on the minimum tone burst power. (Item 18) 17. The method of claim 16, further comprising ejecting droplets from the reservoir by applying focused acoustic energy to the reservoir according to the toneburst pattern. (Item 19) 20. The method of claim 18, further comprising detecting the ejection of the droplet via an optical sensor. (Item 20) repeatedly varying one of the power, wavelength, or duration of one or more of the first, second, or third toneburst segments until a droplet ejection is detected; 20. The method of claim 18 or 19, further comprising setting the power, wavelength, or duration based on the detection of droplet ejection. (Item 21) 20. The method of claim 18 or 19, further comprising predicting a droplet volume associated with the ejected droplet by processing the set of echo signals with a convolutional neural network trained based on a comparison data set including a plurality of reflected acoustic signals corresponding to a respective plurality of non-Newtonian fluids having known properties. (Item 22) determining an actual drop volume associated with the ejected drop; and comparing the actual droplet volume with the predicted droplet volume; 22. The method of claim 21, further comprising modifying the convolutional neural network based on the comparison. (Item 23) determining a droplet volume associated with the ejected droplet; comparing the droplet volume to a predicted droplet volume; 20. The method of claim 18 or 19, further comprising adjusting the power, wavelength, or duration based on the comparison. (Item 24) 20. The method of any one of claims 16 to 19, further comprising determining a property of the non-Newtonian fluid based on the collected series of echo signals. (Item 25) comparing said series of echo signals with stored echo data comprising a collection of previously received echoes correlated with a known collection of non-Newtonian fluids; 20. The method of any one of claims 16 to 19, further comprising: setting the one of the power, wavelength, or duration based at least in part on the comparison. (Item 26) 20. The method of any one of claims 16 to 19, further comprising determining a property of the non-Newtonian fluid by processing the one or more echo signals with a convolutional neural network trained based on a comparison data set including a plurality of reflected acoustic signals corresponding to a respective plurality of non-Newtonian fluids having known properties. (Item 27) 1. A method for ejecting droplets containing a non-Newtonian fluid from a fluid reservoir, the method comprising: applying a toneburst pattern of focused acoustic energy to a fluid reservoir containing a non-Newtonian fluid, the application ejecting droplets of the non-Newtonian fluid from the fluid reservoir, the toneburst pattern comprising: 1. separate tone burst segments, The first separate The second tone burst segment is applied following the separate tone burst segments, and The second separate The third tone burst segment is applied following the separate The method includes applying a tone burst segment of (Item 28) 28. The method of claim 27, wherein the first tone burst segment has a longer duration than the second and third segments, and the third segment has a longer duration than the second segment. (Item 29) The first separate causing leading protrusions and filaments to emerge from a fluid surface of the non-Newtonian fluid by a toneburst of The second and third separate and causing droplet ejection by separating the leading projection from the filament by a tone burst of. (Item 30) 30. The method of claim 29, wherein the filament comprises at least one bead of fluid, and the toneburst pattern prevents the at least one bead of fluid from breaking up into one or more additional droplets. (Item 31) 30. The method of claim 29, wherein the filaments of the non-Newtonian fluid comprise strands including one or more beads of the non-Newtonian fluid. (Item 32) 32. The method of claim 31, wherein the toneburst pattern induces droplet breakoff from the chain. (Item 33) 32. The method of claim 31, wherein the filament comprises 1, 2, or >2 beads of the non-Newtonian fluid. (Item 34) 32. The method of claim 31, wherein the filament comprises 5, 10, or >10 beads of the non-Newtonian fluid. (Item 35) 32. The method of claim 31, wherein the bead of non-Newtonian fluid has a thickness that exceeds the diameter of the filament by 10%, 20%, 50%, or <50% of the diameter of the filament. (Item 36) 32. The method of claim 31, wherein the beads of the non-Newtonian fluid have a thickness no greater than 50%, 75%, or 90% of the resulting droplet diameter. (Item 37) 32. The method of claim 31, wherein the beads on the chain coalesce with each other or with the non-Newtonian fluid in the reservoir before or after droplet breakoff. (Item 38) 32. The method of claim 31, wherein the droplet volume of the ejected droplet is less than 80%, less than 10%, or less than 5% of the remaining volume of the filament during droplet ejection. (Item 39) 32. The method of claim 31, wherein the ejection of droplets occurs when the filament has a length of at least 5 droplet diameters, at least 10 droplet diameters, at least 20 droplet diameters, at least 50 droplet diameters, or at least 100 droplet diameters. (Item 40) 29. The method of claim 27 or 28, wherein the droplets have a diameter less than 70%, less than 30%, or less than 10% of the length of the filament. (Item 41) 29. The method of claim 27 or 28, wherein the droplet has a droplet volume of less than 80%, preferably less than 10%, preferably less than 5% of the remaining volume of the filament during droplet ejection. (Item 42) 29. The method of claim 27 or 28, wherein the amplitude of the tone burst exceeds a displacement amplitude associated with ejecting a displacement drop from a Newtonian fluid. (Item 43) 29. The method of claim 27 or 28, wherein an amplitude of the toneburst varies between the first and second toneburst segments or between the second and third toneburst segments. (Item 44) 32. The method of claim 31, further comprising optically or electronically monitoring the filament to assess whether the droplet detachment was successful or unsuccessful. (Item 45) 29. The method of claim 27 or 28, further comprising predicting droplet detachment based on an optical or electrical signal from a fluid mound formed in the reservoir. (Item 46) 29. The method of claim 27 or 28, wherein the non-Newtonian fluid comprises a solution containing genetic material. (Item 47) applying a series of calibration tone bursts to a fluid surface configured to dispense a volume of fluid from the fluid surface; detecting the formation of filaments in the volume of fluid ejected from the fluid surface; 29. The method of claim 27 or claim 28, further comprising setting one of a power, a wavelength, or a duration of one or more toneburst segments of the toneburst pattern based on the detection of the filament. (Item 48) 1. A droplet ejection system configured to eject droplets from a non-Newtonian fluid in a reservoir, the system comprising: an acoustic ejector configured to apply a toneburst pattern of focused acoustic energy to the reservoir such that the focused acoustic energy ejects droplets of the non-Newtonian fluid from the reservoir; and a memory device storing executable instructions that, when executed by a processor, cause the acoustic ejector to apply the focused acoustic energy in a pattern, the pattern comprising: 1. separatetone burst segments, The first separate The second tone burst segment is applied following the separate tone burst segments, and The second separate The third tone burst segment is applied following the separate A droplet ejection system comprising:

Claims

1. 1. A method for ejecting droplets from a reservoir containing a non-Newtonian fluid by an acoustic droplet ejector, the non-Newtonian fluid being a mixture of water and genomic DNA, the method comprising: applying focused acoustic energy to a fluid surface of the non-Newtonian fluid in a first pattern including first toneburst segments to generate the leading protrusion and the filament from the fluid surface without separating the filament from the leading protrusion or the fluid surface; applying focused acoustic energy to the leading protrusion in a second pattern including second and third toneburst segments to eject a droplet by separating the leading protrusion from the filament to form the droplet; an amplitude of the focused acoustic energy varies between the first and second toneburst segments or between the second and third toneburst segments; the first toneburst segment having an amplitude and duration sufficient to project the leading protrusion and filament from the fluid surface; the first toneburst segment having a longer duration than each one of the second toneburst segment and the third toneburst segment, the third toneburst segment having a longer duration than the second toneburst segment.

2. The method of claim 1, wherein the second pattern includes at least two tone burst segments, including the second tone burst segment and the third tone burst segment configured to separate the droplet from the filament.

3. 3. The method of claim 1, wherein the filament comprises at least one bead of fluid, and the second pattern of focused acoustic energy prevents the at least one bead of fluid from breaking up into one or more additional droplets.

4. 3. The method of claim 1, wherein the droplets have a diameter that is less than 70%, less than 30%, or less than 10% of the length of the filament, or the droplets have a droplet volume that is less than 80%, less than 10%, or less than 5% of the total volume of the remainder of the filament.

5. The method of any one of claims 1 to 2, wherein the second pattern of focused acoustic energy is configured to effect droplet ejection without generating satellite droplets.

6. the filament of the non-Newtonian fluid produced from the reservoir by the focused acoustic energy comprises a strand containing 0, 1, 2, or >2 beads of the non-Newtonian fluid; The method of any one of claims 1 to 2, wherein the second pattern of focused acoustic energy induces droplets to break off from the chains.

7. The method of claim 6 , wherein the beads on the chain coalesce with the non-Newtonian fluid in the reservoir before or after droplet breakoff.

8. 7. The method of claim 6, further comprising optically or electronically monitoring the filament to assess whether the droplet detachment is successful or unsuccessful.

9. The method of claim 1 , further comprising predicting droplet detachment based on optical or electrical signals from fluid mounds formed in the reservoir.

10. applying a series of calibration tone bursts to the fluid surface configured to eject a portion of the non-Newtonian fluid from the fluid surface; detecting the formation of a filament in the portion of the non-Newtonian fluid ejected from the fluid surface or detecting the ejection of a calibration droplet from the fluid surface; 3. The method of claim 1, further comprising: setting one of a power, a wavelength, or a duration of one or more tone bursts of the first pattern of focused acoustic energy or the second pattern of focused acoustic energy based on the detection of the filament or the ejection of the calibration droplet.

11. 1. A droplet ejection system configured to eject droplets from a non-Newtonian fluid in a reservoir, the non-Newtonian fluid being a mixture of water and genomic DNA, the system comprising:

1. An acoustic ejector, comprising: applying focused acoustic energy to a fluid surface of the non-Newtonian fluid in a first pattern including first toneburst segments to generate the leading protrusion and the filament from the fluid surface without separating the filament from the leading protrusion or the fluid surface; and an acoustic ejector configured to apply focused acoustic energy in a second pattern including second and third toneburst segments to the leading protrusion to separate the leading protrusion from the filament without ejecting satellite droplets such that the filament is drawn into the fluid surface; a processor and a memory device storing executable instructions, which when executed by the processor, cause the acoustic ejector to apply the focused acoustic energy to a fluid surface of the non-Newtonian fluid in the first pattern and to apply focused acoustic energy to the leading protrusion in the second pattern; an amplitude of the focused acoustic energy varies between the first and second toneburst segments or between the second and third toneburst segments; the first toneburst segment having an amplitude and duration sufficient to project the leading protrusion and filament from the fluid surface; a droplet ejection system, the droplet ejection system comprising: a first toneburst segment having a longer duration than each one of the second toneburst segment and the third toneburst segment; and a third toneburst segment having a longer duration than the second toneburst segment.

12. 1. A method for setting parameters of a toneburst pattern for droplet ejection from a non-Newtonian fluid, the non-Newtonian fluid being a mixture of water and genomic DNA, the method comprising: applying a series of calibration tone bursts to a fluid surface configured to eject a portion of the non-Newtonian fluid from the fluid surface; acquiring a series of echo signals corresponding to said series of calibration tone bursts; setting power, wavelength, and duration of first through third toneburst segments of a toneburst pattern of focused acoustic energy configured to eject droplets from a reservoir containing the non-Newtonian fluid based on the collected series of echo signals, the first toneburst segment ejecting a leading protrusion and a filament, and the second and third toneburst segments separating the leading protrusion from the filament without ejecting satellite droplets such that the filament is drawn into the fluid surface; an amplitude of the focused acoustic energy varies between the first and second toneburst segments or between the second and third toneburst segments; the first toneburst segment having an amplitude and duration sufficient to project the leading protrusion and filament from the fluid surface; the first toneburst segment having a longer duration than each one of the second toneburst segment and the third toneburst segment, the third toneburst segment having a longer duration than the second toneburst segment.

13. determining a minimum toneburst power corresponding to droplet ejection based on the series of collected echo signals; 13. The method of claim 12, further comprising: setting one of the power, wavelength, or duration based at least in part on the minimum tone burst power.

14. 13. The method of claim 12, further comprising ejecting a droplet from the reservoir by applying focused acoustic energy to the reservoir in accordance with the toneburst pattern and detecting the ejection of the droplet via an optical sensor.

15. 15. The method of claim 14, further comprising predicting a droplet volume associated with the ejected droplet by processing the set of echo signals with a convolutional neural network trained based on a comparison data set including a plurality of reflected acoustic signals corresponding to a respective plurality of non-Newtonian fluids having known properties.

16. determining an actual drop volume associated with the ejected drop; and comparing the actual droplet volume with the predicted droplet volume; The method of claim 15 , further comprising: modifying the convolutional neural network based on the comparison.

17. determining a droplet volume associated with the ejected droplet; comparing the droplet volume to a predicted droplet volume; The method of claim 14 , further comprising: adjusting the power, wavelength, or duration based on the comparison.

18. comparing said series of echo signals with stored echo data comprising a collection of previously received echoes correlated with a known collection of non-Newtonian fluids; The method of any one of claims 12 to 14, further comprising: setting one of the power, wavelength, or duration based at least in part on the comparison.

19. 15. The method of any one of claims 12 to 14, further comprising determining a characteristic of the non-Newtonian fluid by processing the one or more echo signals with a convolutional neural network trained on a comparison data set including a plurality of reflected acoustic signals corresponding to a respective plurality of non-Newtonian fluids having known characteristics.

20. 1. A method for ejecting droplets containing a non-Newtonian fluid from a fluid reservoir, the non-Newtonian fluid being a mixture of water and genomic DNA, the method comprising: applying a toneburst pattern of focused acoustic energy to a fluid reservoir containing a non-Newtonian fluid, wherein said application of said toneburst pattern to said fluid reservoir ejects droplets of said non-Newtonian fluid from said fluid reservoir, said toneburst pattern comprising: a first tone burst segment; a second toneburst segment applied subsequent to the first toneburst segment; and a third toneburst segment applied subsequent to the second toneburst segment; an amplitude of the focused acoustic energy varies between the first and second toneburst segments or between the second and third toneburst segments; and causing leading protrusions and filaments to emerge from a fluid surface of the non-Newtonian fluid with the first toneburst segment; causing droplet ejection by separating the leading projection from the filament with the second toneburst segment and the third toneburst segment. Including, the first toneburst segment having an amplitude and duration sufficient to project the leading protrusion and filament from the fluid surface; the first toneburst segment having a longer duration than each one of the second toneburst segment and the third toneburst segment, the third toneburst segment having a longer duration than the second toneburst segment.

21. 21. The method of claim 20, wherein the filament comprises at least one bead of fluid, and the toneburst pattern prevents the at least one bead of fluid from breaking up into one or more additional droplets.

22. 21. The method of claim 20, wherein a droplet volume of the ejected droplet is less than 80%, less than 10%, or less than 5% of a remaining volume of the filament during droplet ejection.

23. 1. A method for ejecting droplets containing a non-Newtonian fluid from a fluid reservoir, the non-Newtonian fluid being a mixture of water and genomic DNA, the method comprising: applying a toneburst pattern of focused acoustic energy to a fluid reservoir containing a non-Newtonian fluid, wherein said application of said toneburst pattern to said fluid reservoir ejects droplets of said non-Newtonian fluid from said fluid reservoir, said toneburst pattern comprising: a first tone burst segment; a second toneburst segment applied subsequent to the first toneburst segment; and a third toneburst segment applied subsequent to the second toneburst segment; and causing leading protrusions and filaments to emerge from a fluid surface of the non-Newtonian fluid with the first toneburst segment; causing droplet ejection by separating the leading projection from the filament with the second toneburst segment and the third toneburst segment; the ejection of droplets occurs when the filament has a length of at least 5 droplet diameters, at least 10 droplet diameters, at least 20 droplet diameters, at least 50 droplet diameters, or at least 100 droplet diameters; an amplitude of the focused acoustic energy varies between the first and second toneburst segments or between the second and third toneburst segments; the first toneburst segment having an amplitude and duration sufficient to project the leading protrusion and filament from the fluid surface; the first toneburst segment having a longer duration than each one of the second toneburst segment and the third toneburst segment, the third toneburst segment having a longer duration than the second toneburst segment.

24. 21. The method of claim 20, wherein the droplets have a diameter less than 70%, less than 30%, or less than 10% of the length of the filament.

25. 21. The method of claim 20, wherein the droplet has a droplet volume less than 80%, preferably less than 10%, preferably less than 5% of the remaining volume of the filament during droplet ejection.

26. 21. The method of claim 20, further comprising optically or electronically monitoring the filament to assess whether the droplet detachment is successful or unsuccessful.

27. 21. The method of claim 20, further comprising predicting droplet detachment based on optical or electrical signals from fluid mounds formed in the reservoir. applying a series of calibration tone bursts to the fluid surface configured to eject a portion of the non-Newtonian fluid from the fluid surface; detecting the formation of filaments in the portion of the non-Newtonian fluid ejected from the fluid surface; 21. The method of claim 20, further comprising: setting one of a power, a wavelength, or a duration of one or more toneburst segments of the toneburst pattern based on the detection of the filament.

29. 1. A droplet ejection system configured to eject droplets from a non-Newtonian fluid in a reservoir, the non-Newtonian fluid being a mixture of water and genomic DNA, the system comprising: an acoustic ejector configured to apply a toneburst pattern of focused acoustic energy to the reservoir such that the focused acoustic energy ejects droplets of the non-Newtonian fluid from the reservoir; and a memory device storing executable instructions that, when executed by the processor, cause the acoustic ejector to apply the focused acoustic energy in a pattern, the pattern comprising: a first toneburst segment for projecting a filament comprising a leading protrusion and at least one bead of fluid from a fluid surface of the non-Newtonian fluid; a second toneburst segment applied subsequent to the first toneburst segment; and a third toneburst segment applied subsequent to the second toneburst segment; the second toneburst segment and the third toneburst segment separate the leading projection from the filament without ejecting a satellite droplet such that the filament is drawn into the fluid surface; an amplitude of the focused acoustic energy varies between the first and second toneburst segments or between the second and third toneburst segments; the first toneburst segment having an amplitude and duration sufficient to project the leading protrusion and filament from the fluid surface; The first toneburst segment has a longer duration than each one of the second toneburst segment and the third toneburst segment, and the third toneburst segment has a longer duration than the second toneburst segment.

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