Capillary flow velocity profile analysis on paper-based microfluidic chips for screening oil types using machine learning.

We conceived a novel approach to screen oil types on a wax-printed paper-based microfluidic platform. Various oil samples spontaneously flowed through a micrometer-scale channel via capillary action while their components were filtered and partitioned. The resulting capillary flow velocity profile fluctuated during the flow, which was used to screen oil types. Raspberry Pi camera captured the video clips, and a custom Python code analyzed them to obtain the capillary flow velocity profiles. 106 velocity profiles (each with 125 frames for 5 s) were recorded from various oil samples to build a training database. Principal component analysis (PCA), support vector machine (SVM), and linear discriminant analysis (LDA) were used to classify the oil types into heavy-to-medium crude, light crude, marine fuel, lubricant, and diesel oils. The second-order polynomial SVM model with PCA as a pre-processing step showed the highest accuracy: 90% in classifying crude oils and 81% in classifying non-crude oils. The assay took less than 30 s from the sample to answer, with 5 s of the capillary action-driven flow. This simple and effective assay will allow rapid preliminary screening of oil types, enable early tracking, and reduce the number of suspect samples to be analyzed by laboratory fingerprinting analysis.

Sensitive, smartphone-based SARS-CoV-2 detection from clinical saline gargle samples.

Saliva specimens have drawn interest for diagnosing respiratory viral infections due to their ease of collection and decreased risk to healthcare providers. However, rapid and sensitive immunoassays have not yet been satisfactorily demonstrated for such specimens due to their viscosity and low viral loads. Using paper microfluidic chips and a smartphone-based fluorescence microscope, we developed a highly sensitive, low-cost immunofluorescence particulometric SARS-CoV-2 assay from clinical saline gargle samples. We demonstrated the limit of detection of 10 ag/µL. With easy-to-collect saline gargle samples, our clinical sensitivity, specificity, and accuracy were 100%, 86%, and 93%, respectively, for n = 27 human subjects with n = 13 RT-qPCR positives.

Smartphone-based sensitive detection of SARS-CoV-2 from saline gargle samples via flow profile analysis on a paper microfluidic chip.

Respiratory viruses, especially coronaviruses, have resulted in worldwide pandemics in the past couple of decades. Saliva-based paper microfluidic assays represent an opportunity for noninvasive and rapid screening, yet both the sample matrix and test method come with unique challenges. In this work, we demonstrated the rapid and sensitive detection of SARS-CoV-2 from saliva samples, which could be simpler and more comfortable for patients than existing methods. Furthermore, we systematically investigated the components of saliva samples that affected assay performance. Using only a smartphone, an antibody-conjugated particle suspension, and a paper microfluidic chip, we made the assay user-friendly with minimal processing. Unlike the previously established flow rate assays that depended solely on the flow rate or distance, this unique assay analyzes the flow profile to determine infection status. Particle-target immunoagglutination changed the surface tension and subsequently the capillary flow velocity profile. A smartphone camera automatically measured the flow profile using a Python script, which was not affected by ambient light variations. The limit of detection (LOD) was 1 fg/µL SARS-CoV-2 from 1% saliva samples and 10 fg/µL from simulated saline gargle samples (15% saliva and 0.9% saline). This method was highly specific as demonstrated using influenza A/H1N1. The sample-to-answer assay time was <15 min, including <1-min capillary flow time. The overall accuracy was 89% with relatively clean clinical saline gargle samples. Despite some limitations with turbid clinical samples, this method presents a potential solution for rapid mass testing techniques during any infectious disease outbreak as soon as the antibodies become available.

Direct capture and smartphone quantification of airborne SARS-CoV-2 on a paper microfluidic chip.

SARS, a new type of respiratory disease caused by SARS-CoV, was identified in 2003 with significant levels of morbidity and mortality. The recent pandemic of COVID-19, caused by SARS-CoV-2, has generated even greater extents of morbidity and mortality across the entire world. Both SARS-CoV and SARS-CoV-2 spreads through the air in the form of droplets and potentially smaller droplets (aerosols) via exhaling, coughing, and sneezing. Direct detection from such airborne droplets would be ideal for protecting general public from potential exposure before they infect individuals. However, the number of viruses in such droplets and aerosols is too low to be detected directly. A separate air sampler and enough collection time (several hours) are necessary to capture a sufficient number of viruses. In this work, we have demonstrated the direct capture of the airborne droplets on the paper microfluidic chip without the need for any other equipment. 10% human saliva samples were spiked with the known concentration of SARS-CoV-2 and sprayed to generate liquid droplets and aerosols into the air. Antibody-conjugated submicron particle suspension is then added to the paper channel, and a smartphone-based fluorescence microscope isolated and counted the immunoagglutinated particles on the paper chip. The total capture-to-assay time was <30 min, compared to several hours with the other methods. In this manner, SARS-CoV-2 could be detected directly from the air in a handheld and low-cost manner, contributing to slowing the spread of SARS-CoV-2. We can presumably adapt this technology to a wide range of other respiratory viruses.

Handheld UV fluorescence spectrophotometer device for the classification and analysis of petroleum oil samples.

Oil spills can be environmentally devastating and result in unintended economic and social consequences. An important element of the concerted effort to respond to spills includes the ability to rapidly classify and characterize oil spill samples, preferably on-site. An easy-to-use, handheld sensor is developed and demonstrated in this work, capable of classifying oil spills rapidly on-site. Our device uses the computational power and affordability of a Raspberry Pi microcontroller and a Pi camera, coupled with three ultraviolet light emitting diodes (UV-LEDs), a diffraction grating, and collimation slit, in order to collect a large data set of UV fluorescence fingerprints from various oil samples. Based on a 160-sample (in 5x replicates each with slightly varied dilutions) database this platform is able to classify oil samples into four broad categories: crude oil, heavy fuel oil, light fuel oil, and lubricating oil. The device uses principal component analysis (PCA) to reduce spectral dimensionality (1203 features) and support vector machine (SVM) for classification with 95% accuracy. The device is also able to predict some physiochemical properties, specifically saturate, aromatic, resin, and asphaltene percentages (SARA) based off linear relationships between different principal components (PCs) and the percentages of these residues. Sample preparation for our device is also straightforward and appropriate for field deployment, requiring little more than a Pasteur pipette and not being affected by dilution factors. These properties make our device a valuable field-deployable tool for oil sample analysis.

Interfacial Effect-Based Quantification of Droplet Isothermal Nucleic Acid Amplification for Bacterial Infection.

Bacterial infection is a widespread problem in humans that can potentially lead to hospitalization and morbidity. The largest obstacle for physicians/clinicians is the time delay in accurately identifying infectious bacteria, especially their sub-species, in order to adequately treat and diagnose such infected patients. Loop-mediated amplification (LAMP) is a nucleic acid amplification method that has been widely used in diagnostic applications due to its simplicity of constant temperature, use of up to 4 to 6 primers (rendering it highly specific), and capability of amplifying low copies of target sequences. Use of interfacial effect-based monitoring is expected to dramatically shorten the time-to-results of nucleic acid amplification techniques. In this work, we developed a LAMP-based point-of-care platform for detection of bacterial infection, utilizing smartphone measurement of contact angle from oil-immersed droplet LAMP reactions. Whole bacteria (Escherichia coli O157:H7) were assayed in buffer as well as 5% diluted human whole blood. Monitoring of droplet LAMP reactions was demonstrated in a three-compartment, isothermal proportional-integrated-derived (PID)-controlled chip. Smartphone-captured images of droplet LAMP reactions, and their contact angles, were evaluated. Contact angle decreased substantially upon target amplification in both buffer and whole blood samples. In comparison, no-target control (NTC) droplets remained stable throughout the 30 min isothermal reactions. These results were explained by the pre-adsorption of plasma proteins to an oil-water interface (lowering contact angle), followed by time-dependent amplicon formation and their preferential adsorption to the plasma protein-occupied oil-water interface. Time-to-results was as fast as 5 min, allowing physicians to quickly make their decision for infected patients. The developed assay demonstrated quantification of bacteria concentration, with a limit-of-detection at 102 CFU/µL for buffer samples, and binary target or no-target identification with a limit-of-detection at 10 CFU/µL for 5% diluted whole blood samples.