Thermoplastics offer a scalable and cost-effective platform for fabricating microfluidic devices for point-of-care (POC) diagnostics. Among them, cyclic olefin polymer (COP) stands out due to its exceptional optical clarity, making it suitable for fluorescence-based biosensing. However, bonding COP layers without compromising mechanical strength or imaging quality remains a challenge. This study systematically evaluates four bonding techniques: thermal bonding, solvent bonding, UV-curable adhesives, and pressure-sensitive adhesives (PSA), using standardized 180-degree peel tests to quantify bond strength. PSA bonding exhibited the highest adhesion (0.2–0.8 N/mm), while thermal and solvent bonds were notably weaker (<0.05 N/mm). Plasma treatment improved bond strength and uniformity across most methods. We demonstrate the utility of these bonding techniques by fabricating multilayer microfluidic chips compatible with particle diffusometry (PD), an optical biosensing method that detects Vibrio cholerae DNA via changes in nanoparticle motion. Chips were assessed for both mechanical robustness and image intensity suitability for smartphone-based PD measurements. This work provides practical design criteria for selecting prototyping-compatible bonding strategies in the development of low-cost, optically clear microfluidic diagnostic platforms.

Micro and nano-scale colloidal particles can be rapidly assembled at electrode-electrolyte interfaces in highly organized structures. These particles aggregate through various forces which are, among others, chemical, electrical, and thermal in nature. Patterning biological cells in such structures has high impact applications but it remains a challenge due to their limited viability under said manipulation forces. Rapid electrokinetic patterning (REP) uses AC electrothermal micro-vortices to aggregate both synthetic particles and biological cells. In this work, we explore the effects of a DC offset on a REP trap performance in bio-relevant isotonic medium. REP traps were characterized by measuring the inter-particle distance under different DC offsets, using a Delaunay triangulation. The inter-particle distance was measured in a steady-state trap followed by the disassembly of the aggregate as the REP vortex was turned off. DC offset enhanced the trapping performance for micro-particles suspended in a sugar-based isotonic medium. It was observed that an increasingly negative DC offset increased the steady-state inter-particle distance and reduced the rate of disassembly. However, no such trend was found with positive DC offsets. Changing the offset in small steps (<500 mV) affected only the inter-particle distance whereas, larger steps significantly affected the trap stability and size.

Particle diffusometry (PD) is a technique of measuring the diffusion coefficient of a fluid sample by seeding it with tracer particles and observing their motion under a microscope. In microfluidic set-ups, the observed particles are often defocused and their motion is affected by factors such as fluid flow, which leads to high errors for conventional and deep learning-based PD (DPD) algorithms. This work improves the performance of DPD models by updating their architecture, avoiding temporal averaging in the input, and exploring the impact of various choices during training. These models provide state-of-the-art performance for generalised datasets regardless of particle shapes, concentration, flow or image noise and are called DPD-v2. These models provide a mean absolute error of 0.09 $\mu$ m2s−1 for Gaussian particles and 0.07 $\mu$ m2s−1 for defocused particles, which is 2x–4x lower errors as compared with the two following best methods. The performance of DPD-v2 models increases with crop size and the use of multiple stacks of images. The outputs of the DPD-v2 models were compared against the outputs from conventional algorithms on Gaussianised experimental no flow datasets, which provided < 0.5 $\mu$ m2s−1 mean absolute difference. Hence, the DPD-v2 models can be used in real-world scenarios.

Immobilization of proteins and enzymes on solid supports has been utilized in a variety of applications, from improved protein stability on supported catalysts in industrial processes to fabrication of biosensors, biochips, and microdevices. A critical requirement for these applications is facile yet stable covalent conjugation between the immobilized and fully active protein and the solid support to produce stable, highly bio-active conjugates. Here, we report functionalization of solid surfaces (gold nanoparticles and magnetic beads) with bio-active proteins using site-specific and biorthogonal labeling and azide-alkyne cycloaddition, a click chemistry. Specifically, we recombinantly express and selectively label calcium-dependent proteins, calmodulin and calcineurin, and cAMP-dependent protein kinase A (PKA) with N-terminal azide-tags for efficient conjugation to nanoparticles and magnetic beads. We successfully immobilized the proteins on to the solid supports directly from the cell lysate with click chemistry, forgoing the step of purification. This approach is optimized to yield low particle aggregation and high levels of protein activity post-conjugation. The entire process enables streamlined workflows for bioconjugation and highly active conjugated proteins.

Diffusion coefficient measurement is a helpful tool in revealing various properties of a fluid such as viscosity and temperature. However, determining the diffusion coefficient often requires specialized equipment. Particle-based techniques allow the use of conventional cameras to determine flow properties without any specialized measurement devices. However, the performance of existing methods such as single-particle and correlation-based measurements degrade drastically in the presence of real-world scenarios such as flow and thermal gradients. This work introduces a new method of estimating diffusion coefficient in the presence of flow and thermal gradients named deep particle diffusometry (DPD). The technique uses temporally averaged particle images as inputs and uses convolutional neural networks to predict the underlying diffusion coefficient. The results show that a high fit coefficient R 2 value of 0.99 was achieved with no or known fluid flow conditions and an R 2 value of 0.95 was achieved if the fluid had an arbitrary flow. Next, the generalization ability of the network was shown by training the DPD models on no gradient datasets and testing on datasets with a diffusion coefficient gradient. The networks maintained comparably high R 2 values of 0.96. Next, the DPD models were tested against three conventional methods on various simulated datasets, showing their superior performance in situations where an arbitrary flow was present along with diffusion. Finally, the networks were tested on experimental data and the predictions were compared with conventional methods which resulted in R2 values of 0.97 under the no-flow condition. The results show that the proposed method provides performance similar to existing methods on datasets with no flow or with a known flow and can surpass their performance on datasets that have an arbitrary flow.

This paper describes the effect of optical light on the generation and manipulation of microvortex flow named 'twin opposing microvortex' (TOMV) flow. This opto-electrohydrodynamic (OEHD) technique combines optical light, i.e. infrared (IR) laser (1064 nm), with non-uniform AC electric fields generated from a pair of indium tin oxide (ITO) electrodes. When the IR laser beam passes through the electric fields, a rapid and three-dimensional (3D) vortex flow is generated in a microchamber. When the laser beam passes through the electric fields, especially the exposed ITO electrode, the direction of the TOMV flow as well as its strength are controlled. With an AC signal of 107 kHz and various voltages below a peak-to-peak voltage of 10 V, laser power is varied up to 1.5 W and the path of a laser beam relative to the electrode (300 mu m long and 16 mu m wide) is manipulated. The maximum in-plane velocity outside the electrode region was obtained by micron-resolution particle image velocimetry (mu PIV). When the laser beam passes through the left or right side of the lower electrode, the TOMV flow field rotates counterclockwise or clockwise, respectively. Applying optical light on an ITO electrode creates in situ and on-demand microvortex flow, which increases the feasibility of OEHD technique in various biological and chemical applications (e.g., mixing and delivering nanofluids in microfluidic devices).

Diffusion is a natural phenomenon in fluids. Its measurement can be done optically by seeding an otherwise featureless fluid with tracer particles and observing their motion. However, existing algorithms for particle-based diffusion coefficient measurement have multiple failure modes especially when the fluid has a flow or the particles are defocused. We present a method based on Convolutional Neural Networks (CNNs) for predicting diffusion coefficients in the presence of these real-world effects. The CNNs were trained, validated, and tested on crops from four-frame temporally averaged images. The study includes three simulated datasets: Gaussian-shaped particles under no fluid flow condition, Gaussian-shaped particles under an arbitrary flow condition, and defocused particles under no fluid flow condition. The results show that the CNNs have a low Mean Absolute Error (MAE) of 0.12 µm 2 /s between the true and predicted diffusion coefficient values for the dataset with Gaussian-shaped particles under no fluid flow condition. The CNNs have a slightly higher MAE of 0.19 µm^2 /s for Gaussian-shaped particles under arbitrary flow and defocused particles under no fluid flow conditions. The performance of the CNNs was also benchmarked against four conventional algorithms on these simulated datasets. The results show that the conventional algorithms perform better than the CNNs when the particles are assumed to be Gaussian and the fluid has no flow. However, the CNNs outperform conventional methods in the presence of flow or if the particles are defocused. Finally, the outputs of CNNs were compared against the outputs of conventional algorithms on experimental datasets. This provides uncertainty in the range of 0.19 µm^2 /s - 0.47 µm^2 /s, which is slightly larger than the errors obtained from simulated datasets. Hence, the study shows that CNNs can be used to reliably predict diffusion coefficients from complex particle datasets with as little as four frames.

Microfluidics has enabled the analysis of several biochemical phenomena crucial to life as we know it. Encapsulation of target molecules, cells, or synthetic particles in micro and nanodroplets enables sensitive single-cell analysis. Droplet microfluidic technologies reduce the quantities of the required target molecules, which are often limited, whereas digital microfluidic technologies enable easy and repeatable manipulation of these droplets. Various mass spectrometry techniques such as electrospray ionization (ESI) and matrix-assisted or surface-assisted laser desorption-ionization (MALDI or SALDI) have resulted in pioneering research in the conception and detection of novel chemical compounds, proteins, peptides, and metabolites. Biological cell-sorting technologies such as fluorescence-activated cell sorting (FACS) have promoted multiparameter cell detection and sorting. Micromanipulation techniques such as rapid electrokinetic patterning (REP), optoelectrowetting (OEW), and others employ noninvasive optically activated electrokinetic traps to capture, transport, sort, and deposit synthetic particles and biological cells. Integration of these technologies with droplet and digital microfluidics has extended the frontiers of many fields with biochemical applications ranging from (but not limited to) pharmaceutics and sustainable agriculture to multiomics and CRISPR technologies. This chapter discusses many such microfluidic tools advancing the fields of mass spectrometry, FACS, and noninvasive micromanipulation.

Isothermal nucleic acid amplification tests, NAATs, such as reverse transcription-loop-mediated isothermal amplification (RT-LAMP), offer promising capabilities to perform real-time semiquantitative detection of viral pathogens. These tests provide rapid results, utilize simple instrumentation for single-temperature reactions, support efficient user workflows, and are suitable for field use. Herein, we present a novel and robust method for real-time monitoring of HIV-1 RNA RT-LAMP utilizing a novel implementation of particle diffusometry (PD), a diffusivity quantification technique using fluorescent particles, to quantify viral concentration in nuclease-free water. We monitor changes in particle diffusion dynamics of 400 nm fluorescently labeled particles throughout the RT-LAMP of HIV-1 RNA in nuclease-free water, enabling measurement within 20 min and detection of concentrations as low as 25 virus particles per μL. Moreover, in a single-blind study, we demonstrate semiquantitative detection by accurately determining the initial concentration of an unknown HIV-1 RNA within a 10% absolute error margin. These results highlight the potential of real-time PD readout for quantifying HIV-1 RNA via RT-LAMP, offering promise for viral load monitoring of HIV and other chronic infections.

Three-dimensional defocusing particle tracking velocimetry (3D DPTV) is an emerging tool for investigating fluid motion within microfluidic devices, where observation is typically limited to a single direction. This technique enables the reconstruction of 3D flow fields by analyzing the defocused particle images. The accuracy of 3D DPTV significantly relies on the collection of a proper calibration image stack containing particle images with precisely known defocus distances. One major challenge is to insert a microscale calibration target inside the microfluidic devices due to the small length scales of microfluidic devices. While the alternative approaches such as using tilted glass slides or conducting z-scans of particles fixed at the bottom wall, face several limitations. Here, we present the application of a two-photon polymerization (2PP) based method to manufacture a microscale reference ramp as the calibration target. We demonstrate the fabrication of a precise reference ramp structure that spans the desired calibration range to enhance accuracy in 3D defocusing particle tracking. We experimentally and computationally compare the result with the commonly employed methods to obtain the calibration image stack. The performance of the 2PP-based calibration ramps is compared with the z-scanning method using the General Particle tracking (GPTV). The results demonstrate that the z-scanning method underestimates the defocused distances compared to the ground truth provided by the ramp approach, with the error increasing as the defocused distance grows. The integration of microscale calibration targets using 2PP fabrication method offers a significant advancement in the reliability and accuracy of 3D DPTV measurements in microfluidic systems. By enabling precise, device-specific calibration, this approach can enhance the fidelity of flow field reconstructions, benefiting a wide range of microfluidic applications that rely on detailed velocity field information.

This work presents a straightforward computational method to estimate the rotational diffusion coefficient (Dr) of cells and particles of various sizes using the continuum fluid mechanics theory. We calculate the torque (Γ) for cells and particles immersed in fluids to find the mobility coefficient μ and then obtain the Dr by substituting Γ in the Einstein relation. Geometries are constructed using triangular mesh, and the model is solved with computational fluid dynamics techniques. This method is less intensive and more efficient than the widely used models. We simulate eight different particle geometries and compare the results with previous literature.

Particle diffusometry, a technology derived from particle image velocimetry, quantifies the Brownian motion of particles suspended in a quiescent solution by computing the diffusion coefficient. Particle diffusometry has been used for pathogen detection by measuring the change in solution viscosity due to amplified DNA from a specific gene target. However, particle diffusometry fails to calculate accurate measurements at elevated temperatures and fluid flow. Therefore, these two current limitations hinder the potential application where particle diffusometry can further be used. In this work, we expanded the usability of particle diffusometry to be applied to fluid samples with simple shear flow and at various temperatures. A range of diffusion coefficient videos is created to simulate the Brownian motion of particles under flow and temperature conditions. Our updated particle diffusometry analysis forms a correction equation under three different polynomial degrees of shear flow with varying flow rates and temperatures between 25 and 65 °C. An experiment in a channel with a rectangular cross section using a syringe pump to generate a constant flow is done to analyze the modified algorithm. In simulation analysis, the modified algorithm successfully computes the diffusion coefficients with ± 10 pixel/Δ t on all three flow types. Complementary experiments confirm the simulation results.

Real-time viscosity measurement techniques have been used to analyze the transition of hydrogels from a liquid state to a gel state. As viscosity is inversely proportional to diffusion coefficient, measuring real-time changes in viscosity can be done through passive rheometry with the addition of tracer particles. Particle diffusometry (PD) quantifies Brownian motion of sub-micron sized fluorescent particles by computing diffusion coefficients via statistical averaging. Herein, we demonstrate a method to study changes in diffusion coefficient as a function of time using PD for a temporally and spatially resolved rheometry measurement technique. We refined the PD algorithm using synthetic images of particles suspended in a liquid undergoing a sigmoidally decreasing diffusion trend to simulate the viscosity change of the solution during gelation. Then, the technique is applied to visualize the temporal and spatial gradients of diffusion coefficient during polyacrylamide hydrogel formation experiments. This work establishes the groundwork for quantifying over time changes in Brownian motion.

Diffusion coefficient measurement is a helpful tool in revealing various properties of a fluid such as viscosity and temperature. However, determining the diffusion coefficient often requires specialized equipment. Particle-based techniques allow the use of conventional cameras to determine flow properties without any specialized measurement devices. However, the performance of existing methods such as single-particle and correlation-based measurements degrade drastically in the presence of real-world scenarios such as flow and thermal gradients. This work introduces a new method of estimating diffusion coefficient in the presence of flow and thermal gradients named deep particle diffusometry (DPD). The technique uses temporally averaged particle images as inputs and uses convolutional neural networks to predict the underlying diffusion coefficient. The results show that a high fit coefficient R 2 value of 0.99 was achieved with no or known fluid flow conditions and an R 2 value of 0.95 was achieved if the fluid had an arbitrary flow. Next, the generalization ability of the network was shown by training the DPD models on no gradient datasets and testing on datasets with a diffusion coefficient gradient. The networks maintained comparably high R 2 values of 0.96. Next, the DPD models were tested against three conventional methods on various simulated datasets, showing their superior performance in situations where an arbitrary flow was present along with diffusion. Finally, the networks were tested on experimental data and the predictions were compared with conventional methods which resulted in R 2 values of 0.97 under the no-flow condition. The results show that the proposed method provides performance similar to existing methods on datasets with no flow or with a known flow and can surpass their performance on datasets that have an arbitrary flow.

Non-contact micro-manipulation tools have enabled invasion-free studies of fragile synthetic particles and biological cells. Rapid electrokinetic patterning (REP) traps target particles/cells, suspended in an electrolyte, on an electrode surface. This entrapment is electrokinetic in nature and thus depends strongly on the suspension medium's properties. REP has been well characterized for manipulating synthetic particles suspended in low concentration salt solutions (~ 2 mS/m). However, it is not studied as extensively for manipulating biological cells, which introduces an additional level of complexity due to their limited viability in hypotonic media. In this work, we discuss challenges posed by isotonic electrolytes and suggest solutions to enable REP manipulation in bio-relevant media. Various formulations of isotonic media (salt and sugar-based) are tested for their compatibility with REP. REP manipulation is observed in low concentration salt-based media such as 0.1× phosphate buffered saline (PBS) when the device electrodes are passivated with a dielectric layer. We also show manipulation of murine pancreatic cancer cells suspended in a sugar-based (8.5% w/v sucrose and 0.3% w/v dextrose) isotonic medium. The ability to trap mammalian cells and deposit them in custom patterns enables high-impact applications such as determining their biomechanical properties and 3D bioprinting for tissue scaffolding.

The measurement and optimization of protein-protein interactions are critical in the design of biotherapeutics, biomolecular sensing elements, and functional protein-based biomaterials among other biomolecular sciences and engineering. Current gold standard assays require specifically designed core facilities, equipment, and expertise to implement the measurement, making it inconvenient for most labs unless implemented routinely. We developed a new method aiming at measuring protein binding kinetics based on microfluidics and particle diffusometry (PD), which only needs very general lab equipment, including a fluorescence microscope, a syringe pump, and a simple microchannel fabricated on a glass slide. Protein binding pairs are immobilized on two kinds of nanoparticles with different diameters using widely available conjugation chemistries. The two diluted particle suspensions are injected using a syringe pump into a Y-junction microchannel, where they bind and form particle complexes with increasing size, thereby decreasing particles' Brownian motion amplitude and diffusivity, which can be detected by PD. By taking images at a series of specific points along the microchannel, the particle diffusivity is measured at different time points after the introduction of protein-protein binding. These data are then used to quantify the protein binding kinetic constant. This label-free particle-based method is simple to operate and as accurate as the current gold standard. We demonstrate the feasibility of this accessible method by quantifying the streptavidin-biotin association constant (1.74 ± 0.51 × 107 M-1 s-1), which compares well with previously published results.

In 2019 the COVID-19 pandemic, caused by SARS-CoV-2, demonstrated the urgent need for rapid, reliable, and portable diagnostics. The COVID-19 pandemic was declared in January 2020 and surges of the outbreak continue to reoccur. It is clear that early identification of infected individuals, especially asymptomatic carriers, plays a huge role in preventing the spread of the disease. The current gold standard diagnostic for SARS-CoV-2 is quantitative reverse transcription polymerase chain reaction (qRTPCR) test based on the detection of the viral RNA. While RT-PCR is reliable and sensitive, it requires expensive centralized equipment and is time consuming (~2 h or more); limiting its applicability in low resource areas. The FDA issued Emergency Use Authorizations (EUAs) for several COVID-19 diagnostics with an emphasis on point-of care (PoC) testing. Numerous RT-PCR and serological tests were approved for use at the point of care. Abbott's ID NOW, and Cue Health's COVID-19 test are of particular interest, which use isothermal amplification methods for rapid detection in under 20 min. We look to expand on the range of current PoC testing platforms with a new rapid and portable isothermal nucleic acid detection device. We pair reverse transcription loop mediated isothermal amplification (RT-LAMP) with a particle imaging technique, particle diffusometry (PD), to successfully detect SARS-CoV-2 in only 35 min on a portable chip with integrated heating. A smartphone device is used to image the samples containing fluorescent beads post-RT-LAMP and correlates decreased diffusivity to positive samples. We detect as little as 30 virus particles per mL from a RT-LAMP reaction in a microfluidic chip using a portable heating unit. Further, we can perform RT-LAMP from a diluted unprocessed saliva sample without RNA extraction. Additionally, we lyophilize SARS-CoV-2-specific RT-LAMP reactions that target both the N gene and the ORF1ab gene in the microfluidic chip, eliminating the need for cold storage. Our assay meets specific target product profiles outlined by the World Health Organization: it is specific to SARS- CoV-2, does not require cold storage, is compatible with digital connectivity, and has a detection limit of less than 35 x 104 viral particles per mL in saliva. PD-LAMP is rapid, simple, and attractive for screening and use at the point of care. (c) 2022 Published by Elsevier B.V.

Obstructions in airways result in significant alterations in ventilation distribution and consequently reduce the ventilation to perfusion ratio, affecting gas exchange. This study presents a lumped parameter-based model to quantify the spatial ventilation distribution using constructal theory. An extension of the existing theory is made for the conductive bronchial tree and is represented in matrix frame incorporated with airway admittances. The proposed lung admittance model has a greater advantage over the existing methodologies based on lung impedance, as it can be applicable for both fully and partially blocked regions. We proved the well-posedness of the problem, and the generated matrix is highly sparse in nature. A modified block decomposition method is implemented for symmetric and asymmetric trees of various obstructions 0:20:100% to reduce the memory size. The asymmetry is considered in every left branch of the bronchial tree recursively, following the mathematical relations: Li, 2j=ΓLi, 2j+1 and Di, 2j=ΓDi, 2j+1, where L and D are the length, diameter of the jth branch at ith generation, respectively, for Γ∈0.9:0.01:1.0. It is observed that relative flow rate (Qi,jQi,jhealthy) decreases exponentially with the generation index. In tidal breathing, the regional ventilation pattern is found to vary spatially instead of spatio-temporally. The comparison of our result with the clinical data is found to be accurate when 40% or more obstruction is considered in the proximal region (observed in asthma). Moreover, this predicts an increment of lung impedance by 6%, which can be used for further improvement of clinical observations.

Nanoporous membranes with platinum (Pt) and gold (Au) coated on opposite faces can autonomously pump fluid in the presence of hydrogen peroxide, but the physics is not fully understood. Here, we show with simulation results that the self-pumping flow rate can be considerably increased by avoiding the overlap of electric double layers (EDL) inside pores. Due to catalytic electrochemical reactions on Pt and Au, hydrogen ions (H+) are generated and depleted on opposite sides of the membrane, establishing a self-generated electric field and associated electro-osmotic flow through the pores. By optimizing the pore radius, EDL overlap is avoided and an area-averaged self-pumping flow speed of 23 mu m/s can be achieved, which is 20 times higher than previously reported. By conducting the first-ever physico-chemical computational model of self-pumping membranes, this work reveals the mechanism of self-pumping flow in porous two-sided "Janus " membranes and highlights the potential of developing biomimetic membranes and lab-on-chip devices that can precisely and remotely control fluid flow in pores or channels in an "on/off " manner.

Paper-fluidic devices are a popular platform for point-of-care diagnostics due to their low cost, ease of use, and equipment-free detection of target molecules. They are limited, however, by their lack of sensitivity and inability to incorporate more complex processes, such as nucleic acid amplification or enzymatic signal enhancement. To address these limitations, various valves have previously been implemented in paper-fluidic devices to control fluid obstruction and release. However, incorporation of valves into new devices is a highly iterative, time-intensive process due to limited experimental data describing the microscale flow that drives the biophysical reactions in the assay. In this paper, we tested and modeled different geometries of thermally actuated valves to investigate how they can be more easily implemented in an LFIA with precise control of actuation time, flow rate, and flow pattern. We demonstrate that bulk flow measurements alone cannot estimate the highly variable microscale properties and effects on LFIA signal development. To further quantify the microfluidic properties of paper-fluidic devices, micro-particle image velocimetry was used to quantify fluorescent nanoparticle flow through the membranes and demonstrated divergent properties from bulk flow that may explain additional variability in LFIA signal generation. Altogether, we demonstrate that a more robust characterization of paper-fluidic devices can permit fine-tuning of parameters for precise automation of multi-step assays and inform analytical models for more efficient design.