Multimodal Imaging and Lighting Bias Correction for Improved μPAD-based Water Quality Monitoring via Smartphones

Soil microbiome characterization and its future directions with biosensing

Soil microbiome characterization is typically achieved with next-generation sequencing (NGS) techniques. Metabarcoding is very common, and meta-omics is growing in popularity. These techniques have been instrumental in microbiology, but they have limitations. They require extensive time, funding, expertise, and computing power to be effective. Moreover, these techniques are restricted to controlled laboratory conditions; they are not applicable in field settings, nor can they rapidly generate data. This hinders using NGS as an environmental monitoring tool or an in-situ checking device. Biosensing technology can be applied to soil microbiome characterization to overcome these limitations and to complement NGS techniques. Biosensing has been used in biomedical applications for decades, and many successful commercial products are on the market. Given its previous success, biosensing has much to offer soil microbiome characterization. There is a great variety of biosensors and biosensing techniques, and a few in particular are better suited for soil field studies. Aptamers are more stable than enzymes or antibodies and are more ready for field-use biosensors. Given that any microbiome is complex, a multiplex sensor will be needed, and with large, complicated datasets, machine learning might benefit these analyses. If the signals from the biosensors are optical, a smartphone can be used as a portable optical reader and potential data-analyzing device. Biosensing is a rich field that couples engineering and biology, and applying its toolset to help advance soil microbiome characterization would be a boon to microbiology more broadly.

A smartphone-based approach for comprehensive soil microbiome profiling

The soil microbiome is crucial for nutrient cycling, health, and plant growth. This study presents a smartphone-based approach as a low-cost and portable alternative to traditional methods for classifying bacterial species and characterizing microbial communities in soil samples. By harnessing bacterial autofluorescence detection and machine learning algorithms, the platform achieved an average accuracy of 88% in distinguishing common soil-related bacterial species despite the lack of biomarkers, nucleic acid amplification, or gene sequencing. Furthermore, it successfully identified dominant species within various bacterial mixtures with an accuracy of 76% and three-level soil health identification at an accuracy of 80%-82%, providing insights into microbial community dynamics. The influence of other soil conditions (pH and moisture) was relatively minor, showcasing the platform's robustness. Various field soil samples were also tested with this platform at 80% accuracy compared with the laboratory analyses, demonstrating the practicality and usability of this approach for on-site soil analysis. This study highlights the potential of the smartphone-based system as a valuable tool for soil assessment, microbial monitoring, and environmental management.

Development of a cloud-based flow rate tool for eNAMPT biomarker detection

Increased levels of extracellular nicotinamide phosphoribosyltransferase (eNAMPT) are increasingly recognized as a highly useful biomarker of inflammatory disease and disease severity. In preclinical animal studies, a monoclonal antibody that neutralizes eNAMPT has been generated to successfully reduce the extent of inflammatory cascade activation. Thus, the rapid detection of eNAMPT concentration in plasma samples at the point of care (POC) would be of great utility in assessing the benefit of administering an anti-eNAMPT therapeutic. To determine the feasibility of this POC test, we conducted a particle immunoagglutination assay on a paper microfluidic platform and quantified its extent with a flow rate measurement in less than 1 min. A smartphone and cloud-based Google Colab were used to analyze the flow rates automatically. A horizontal flow model and an immunoagglutination binding model were evaluated to optimize the detection time, sample dilution, and particle concentration. This assay successfully detected eNAMPT in both human whole blood and plasma samples (diluted to 10 and 1%), with the limit of detection of 1-20 pg/mL (equivalent to 0.1-0.2 ng/mL in undiluted blood and plasma) and a linear range of 5-40 pg/mL. Furthermore, the smartphone POC assay distinguished clinical samples with low, mid, and high eNAMPT concentrations. Together, these results indicate this POC assay, which utilizes low-cost materials, time-effective methods, and a straightforward immunoassay (without surface immobilization), may reliably allow rapid determination of eNAMPT blood/plasma levels to advantage patient stratification in clinical trials and guide ALT-100 mAb therapeutic decision-making.

Smartphone based aptasensors as intelligent biodevice for food contamination detection in food and soil samples: Recent advances

Nowadays, the integration of conventional analytical approaches with smartphones has been developed novel, emerging and affordable devices for improving on-site detection platforms in the fields of food safety. Smartphone-based aptasensors as the next generation of portable aptasensing technique has attracted considerable attention as it offers a semi-automated user interface that can be exploited by inexpert characters. Wireless data transferability is an undeniable advantage that home-testing platforms have as well as it can suggest high computational power. In addition, these types of biodevices can provide real-time monitoring in terms of exchanging digital networks in real-time. To elaborate, the ability of smartphones to connect through the Internet is one of the most critical advantages of smartphone-based aptasensor that can be uploaded to Cloud databases and results can be disseminated as spatio-temporal maps across the globe. This review focused on the recent progress and technical breakthroughs of aptasensor on the smartphone as a groundbreaking enterprise in the field of biochemical analysis, importantly in the aspect of the combination of different types of biosensors including electrochemical, optical and colorimetric. In our opinion, this review can broaden our understanding of using smartphones as a portable sensing approach by addressing the current challenges and future perspectives.

A Comprehensive Review on Water Quality Monitoring Devices: Materials Advances, Current Status, and Future Perspective

Water quality monitoring has become more critical in recent years to ensure the availability of clean and safe water from natural aquifers and to understand the evolution of water contaminants across time and space. The conventional water monitoring techniques comprise of sample collection, preservation, preparation, tailed by laboratory testing and analysis with cumbersome wet chemical routes and expensive instrumentation. Despite the high accuracy of these methods, the high testing costs, laborious procedures, and maintenance associated with them don't make them lucrative for end end-users and field testing. As the participation of ultimate stakeholders, that is, common man for water quality and quantity can play a pivotal role in ensuring the sustainability of our aquifers, thus it is essential to develop and deploy portable and user-friendly technical systems for monitoring water sources in real-time or on-site. The present review emphasizes here on possible approaches including optical (absorbance, fluorescence, colorimetric, X-ray fluorescence, chemiluminescence), electrochemical (ASV, CSV, CV, EIS, and chronoamperometry), electrical, biological, and surface-sensing (SPR and SERS), as candidates for developing such platforms. The existing developments, their success, and bottlenecks are discussed in terms of various attributes of water to escalate the essentiality of water quality devices development meeting ASSURED criterion for societal usage. These platforms are also analyzed in terms of their market potential, advancements required from material science aspects, and possible integration with IoT solutions in alignment with Industry 4.0 for environmental application.

Smartphone-based autofluorescence imaging to detect bacterial species on laboratory surfaces

This work demonstrated instantaneous, reagent- and staining-free, smartphone-based autofluorescence detection of bacterial contamination on typical laboratory desk surfaces. Detection was successfully distinguished from protein, salt, and tap water.

Human sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classification

Bacteria identification has predominantly been conducted using specific bioreceptors such as antibodies or nucleic acid sequences. This approach may be inappropriate for environmental monitoring when the user does not know the target bacterial species and for screening complex water samples with many unknown bacterial species. In this work, we investigate the supervised machine learning of the bacteria-particle aggregation pattern induced by the peptide sets identified from the biofilm-bacteria interface. Each peptide is covalently conjugated to polystyrene particles and loaded together with bacterial suspensions onto paper microfluidic chips. Each peptide interacts with bacterial species to a different extent, leading to varying sizes of particle aggregation. This aggregation changes the surface tension and viscosity of the liquid flowing through the paper pores, altering the flow velocity at different extents. A smartphone camera captures this flow velocity without being affected by ambient and environmental conditions, towards a low-cost, rapid, and field-ready assay. A collection of such flow velocity data generates a unique fingerprinting profile for each bacterial species. Support vector machine is utilized to classify the species. At optimized conditions, the training model can predict the species at 93.3% accuracy out of five bacteria: Escherichia coli, Staphylococcus aureus, Salmonella Typhimurium, Enterococcus faecium, and Pseudomonas aeruginosa. Flow rates are monitored for less than 6 s and the sample-to-answer assay time is less than 10 min. The demonstrated method can open a new way of analyzing complex biological and environmental samples in a biomimetic manner with machine learning classification.

Development of miniaturized fluorimetric device for caffeine determination using a smartphone

Caffeine is an element that is consumed worldwide. It is present in many products such as beverages, chocolate, coffee, tea, energy drinks and medicines. Portable 3D devices working together with colorimetric and fluorimetric reactions have been able to determine the presence of caffeine in different kinds of samples. Also, digital image-based methods using smartphones have conferred portability and accessibility to miniaturized devices that are innovative and promising options for quick and low cost analyses. This study proposes a miniaturized fluorimetric device to determine caffeine by digital image using a smartphone. The OpenCamera app was used to capture images that were processed using ImageJ software to obtain RGB channels values. The red (R) channel signal intensity was selected as the analytical response. The device developed was applied to determine caffeine in an energy drink and medicines. The method developed presented a linear range from 100 to 600 mg L⁻¹ of caffeine, and quantification (LOQ) and detection (LOD) limits of 100 mg L⁻¹ and 30.0 mg L⁻¹, respectively. The caffeine concentration found in the products analyzed was 328 mg L⁻¹ (±2.5%) for the energy drink, 345 mg L⁻¹ (±15%) for medicine A and 322 mg L−¹ (±7.3%) for medicine B. The proposed device presented important characteristics such as low cost, required small volumes of reagents and samples, quick analysis, portability and suitable to be applied in complex matrices.

This study proposes a miniaturized fluorimetric device to determine caffeine in an energy drink and medicines by digital image using a smartphone.

Cell phone based colorimetric analysis for point-of-care settings

Cell phones show considerable promise for point-of-care (POC) diagnostic procedures because they are accessible, connected, and computationally powerful. Cell phone image processing methods are being developed for the detection and quantification of a wide range of targets, employing methods from microscopy to fluorescence techniques. However, most of the lab-based biological and biochemical assays still lack a robust and repeatable cell phone analogue. Existing solutions require external smartphone hardware to obtain quantitative results, imposing a design tradeoff between accessibility and accuracy. Here, we develop a cell phone imaging algorithm that enables analysis of assays that would typically be evaluated via spectroscopy. The developed technique uses the saturation parameter of hue-saturation-value color space to enable POC diagnosis. Through the analysis of over 10 000 images, we show that the saturation method consistently outperforms existing algorithms under a wide range of operating field conditions. The performance improvement is also proven analytically via the mathematic relationship between the saturation method and existing techniques. The method presented here is a step forward towards the development of POC diagnostics by reducing the required equipment, improving the limit of detection (LOD), and increasing the precision of quantitative results.

Point-of-care Colorimetric Analysis through Smartphone Video

Point-of-care (POC) tests often rely on smartphone image methods for colorimetric analysis, but the results of such methods are frequently difficult to reproduce or standardize. The problem is aggravated by unpredictable image capture conditions, which pose a significant challenge when low limits of detection (LOD) are needed. Application-specific smartphone attachments are often used to standardize imaging conditions, but there has recently been an interest in equipment-free POC colorimetric analysis. Improved output metrics and preprocessing methods have been developed, but equipment-free imaging often has a high LOD and is inappropriate for quantitative tasks. Additional work is necessary to replace external smartphone attachments with algorithms. Towards this end, we have developed a video processing method that synthesizes many images into a single output metric. We use image features to select the best inputs from a large set of video frames and demonstrate that the resulting output values have a stronger correlation with laboratory methods and a lower standard error. The developed algorithm only requires 20 seconds of video and can easily be integrated with existing processing methods. We apply our algorithm to the NS1-based sandwich ELISA for Zika detection and show that the LOD is two times lower when our video-based method is used.

Phytofabrication of Iron Nanoparticles for Hexavalent Chromium Remediation

Hexavalent chromium is a genotoxic and carcinogenic byproduct of a number of industrial processes, which is discharged into the environment in excessive and toxic concentrations worldwide. In this paper, the synthesis of green iron oxide nanoparticles using extracts of four novel plant species [Pittosporum undulatum, Melia azedarach, Schinus molle, and Syzygium paniculatum (var. australe)] using a "bottom-up approach" has been implemented for hexavalent chromium remediation. Nanoparticle characterizations show that different plant extracts lead to the formation of nanoparticles with different sizes, agglomeration tendencies, and shapes but similar amorphous nature and elemental makeup. Hexavalent chromium removal is linked with the particle size and monodispersity. Nanoparticles with sizes between 5 and 15 nm from M. azedarach and P. undulatum showed enhanced chromium removal capacities (84.1-96.2%, respectively) when compared to the agglomerated particles of S. molle and S. paniculatum with sizes between 30 and 100 nm (43.7-58.7%, respectively) in over 9 h. This study has shown that the reduction of iron salts with plant extracts is unlikely to generate vast quantities of stable zero valent iron nanoparticles but rather favor the formation of iron oxide nanoparticles. In addition, plant extracts with higher antioxidant concentrations may not produce nanoparticles with morphologies optimal for pollutant remediation.

Sub-nanoliter, real-time flow monitoring in microfluidic chips using a portable device and smartphone

The ever-increasing need for portable, easy-to-use, cost-effective, and connected point-of-care diagnostics (POCD) has been one of the main drivers of recent research on lab-on-a-chip (LoC) devices. A majority of these devices use microfluidics to manipulate precisely samples and reagents for bioanalysis. However, filling microfluidic devices with liquid can be prone to failure. For this reason, we have implemented a simple, yet efficient method for monitoring liquid displacement in microfluidic chips using capacitive sensing and a compact (75 mm × 30 mm × 10 mm), low-cost ($60), and battery-powered (10-hour autonomy) device communicating with a smartphone. We demonstrated the concept using a capillary-driven microfluidic chip comprising two equivalent flow paths, each with a total volume of 420 nL. Capacitance measurements from a pair of electrodes patterned longitudinally along the flow paths yielded 17 pL resolution in monitoring liquid displacement at a sampling rate of 1 data/s (~1 nL/min resolution in the flow rate). We characterized the system using human serum, biological buffers, and water, and implemented an algorithm to provide real-time information on flow conditions occurring in a microfluidic chip and interactive guidance to the user.

Challenges in paper-based fluorogenic optical sensing with smartphones

Application of optically superior, tunable fluorescent nanotechnologies have long been demonstrated throughout many chemical and biological sensing applications. Combined with microfluidics technologies, i.e. on lab-on-a-chip platforms, such fluorescent nanotechnologies have often enabled extreme sensitivity, sometimes down to single molecule level. Within recent years there has been a peak interest in translating fluorescent nanotechnology onto paper-based platforms for chemical and biological sensing, as a simple, low-cost, disposable alternative to conventional silicone-based microfluidic substrates. On the other hand, smartphone integration as an optical detection system as well as user interface and data processing component has been widely attempted, serving as a gateway to on-board quantitative processing, enhanced mobility, and interconnectivity with informational networks. Smartphone sensing can be integrated to these paper-based fluorogenic assays towards demonstrating extreme sensitivity as well as ease-of-use and low-cost. However, with these emerging technologies there are always technical limitations that must be addressed; for example, paper's autofluorescence that perturbs fluorogenic sensing; smartphone flash's limitations in fluorescent excitation; smartphone camera's limitations in detecting narrow-band fluorescent emission, etc. In this review, physical optical setups, digital enhancement algorithms, and various fluorescent measurement techniques are discussed and pinpointed as areas of opportunities to further improve paper-based fluorogenic optical sensing with smartphones.

A Capillary Flow Dynamics-Based Sensing Modality for Direct Environmental Pathogen Monitoring

Toward ultra-simple and field-ready biosensors, we demonstrate a novel assay transducer mechanism based on interfacial property changes and capillary flow dynamics in antibody-conjugated submicron particle suspensions. Differential capillary flow is tunable, allowing pathogen quantification as a function of flow rate through a paper-based microfluidic device. Flow models based on interfacial and rheological properties indicate a significant relationship between the flow rate and the interfacial effects caused by target-particle aggregation. This mechanism is demonstrated for assays of Escherichia coli K12 in water samples and Zika virus (ZIKV) in blood serum. These assays achieved very low limits of detection compared with other demonstrated methods (1 log CFU/mL E. coli and 20 pg/mL ZIKV whole virus) with an operating time of 30 s, showing promise for environmental and health monitoring.

Portable detection of trace metals in airborne particulates and sediments via μPADs and smartphone

Particulate matter (PM), a key indicator of air pollution by natural and anthropogenic activities, contributes to a wide spectrum of diseases that lead to a shortening of life expectancy. It has been recognized that trace metals in airborne PM are highly toxic and can be correlated with lesion in respiratory, gastrointestinal, immunological, and hematological systems. Traditional methods for trace metal assay require sophisticated instrumentations and highly trained operators in centralized laboratories. In this work, by integrating the technologies of microfluidic paper-based analytical devices, additive manufacturing, smartphone, and colorimetric sensing, we developed the first smartphone based paper microfluidic platform for portable, disposable, and quantitative measurements of cobalt (Co), copper (Cu), and iron (Fe) in ambient air and street sediments. On a single A4-sized paper, 48 devices were fabricated in under 30 s with a total cost of ∼$1.9. On each device, 12 reaction units were patterned and used for colorimetric tests. Particulate samples from urban ambient air and street sediments were collected, processed, and analyzed. Signals of the on-chip complexation product were recorded using a smartphone camera and processed by a self-developed app on an iOS system. For precisely controlling the object distance, chip position, and luminance, a hand-held 3D cellphone housing was designed and printed. The detection limits of Co, Cu, and Fe were determined to be 8.2, 45.8, and 186.0 ng, while the linear dynamic ranges were calculated to be 8.2-81.6, 45.8-4.58 × 10², and 1.86 × 10²-1.86 × 10³ ng, representing a practically relevant device performance with a significant reduction in the detection cost and time consumption. Trace metals in ambient air and sediments of two cities in China have been quantified portably, thus demonstrating the utility of our system in improving strategies for air pollution control in low-resource settings.

μPAD Fluorescence Scattering Immunoagglutination Assay for Cancer Biomarkers from Blood and Serum

A microfluidic paper analytical device (μPAD) was created for the sensitive quantification of cancer antigens, carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA 19-9), from human whole blood and serum, toward diagnosis and prognosis of colorectal cancer. Anti-CEA and anti-CA 19-9 antibodies were covalently linked to submicron, fluorescent polystyrene particles, loaded, and then dried in the center of the μPAD channel. CEA- or CA 19-9-spiked blood or serum samples were loaded to the inlet of μPAD, and subsequent immunoagglutination changed the fluorescent scatter signals upon ultraviolet (UV) excitation. The total assay time was about 1 min. Detection limits were 1 pg/mL for CEA and 0.1 U/mL for CA 19-9 from both 10% diluted blood and undiluted serum. The use of UV excitation and subsequent fluorescence scattering enabled much higher double-normalized intensities (up to 1.28-3.51, compared with 1.067 with the elastic Mie scatter detection), successful detection in the presence of blood or serum, and distinct multiplex assays with minimum cross-reaction of antibodies. The results with undiluted serum showed the larger dynamic range and smaller standard errors, which can be attributed to the presence of serum proteins, functioning as a stabilizer or a passivating protein for the particles within paper fibers.

Depletion of Cr(VI) from aqueous solution by heat dried biomass of a newly isolated fungus Arthrinium malaysianum: A mechanistic approach

For the first time, the heat dried biomass of a newly isolated fungus Arthrinium malaysianum was studied for the toxic Cr(VI) adsorption, involving more than one mechanism like physisorption, chemisorption, oxidation-reduction and chelation. The process was best explained by the pseudo-second order kinetic model and Redlich-Peterson isotherm with maximum predicted biosorption capacity (Q_m) of 100.69 mg g⁻¹. Film-diffusion was the rate-controlling step and the adsorption was spontaneous, endothermic and entropy-driven. The mode of interactions between Cr(VI) ions and fungal biomass were investigated by several methods [Fourier Transform-Infrared Spectroscopy (FT-IR), X-ray Diffraction (XRD) and Energy-Dispersive X-ray spectroscopy (EDX)]. X-ray Photoelectron Spectroscopy (XPS) studies confirmed significant reduction of Cr(VI) into non-toxic Cr(III) species. Further, a modified methodology of Atomic Force Microscopy was successfully attempted to visualize the mycelial ultra-structure change after chromium adsorption. The influence of pH, biomass dose and contact time on Cr(VI) depletion were evaluated by Response Surface Model (RSM). FESEM-EDX analysis also exhibited arsenic (As) and lead (Pb) peaks on fungus surface upon treating with synthetic solutions of NaAsO₂ and Pb(NO₃)₂ respectively. Additionally, the biomass could also remove chromium from industrial effluents, suggesting the fungal biomass as a promising adsorbent for toxic metals removal.

Technical aspects and challenges of colorimetric detection with microfluidic paper-based analytical devices (μPADs) - A review

Paper-based devices are a leading alternative among the main analytical tools for point-of-care testing, due to their portability, low-cost, and ease-of-use. Colorimetric readouts are the most common method of detection in these microfluidic devices, enabling qualitative, semi-quantitative and fully quantitative analysis of multiple analytes. There is a multitude of ways to obtain a colorimetric output in such devices, including nanoparticles, dyes, redox and pH indicators, and each has unique drawbacks and benefits. There are also multiple variables that impact the analysis of colorimetric reactions in microfluidic paper-based systems, including color homogeneity, image capture methods, and the data handling itself. Here, we present a critical review of recent developments and challenges of colorimetric detection on microfluidic paper-based analytical devices (μPADs), and present thoughts and insights towards future perspectives in the area to improve the use of colorimetric readouts in conjunction with μPADs.

Evaluation of the SeedCounter, A Mobile Application for Grain Phenotyping

Grain morphometry in cereals is an important step in selecting new high-yielding plants. Manual assessment of parameters such as the number of grains per ear and grain size is laborious. One solution to this problem is image-based analysis that can be performed using a desktop PC. Furthermore, the effectiveness of analysis performed in the field can be improved through the use of mobile devices. In this paper, we propose a method for the automated evaluation of phenotypic parameters of grains using mobile devices running the Android operational system. The experimental results show that this approach is efficient and sufficiently accurate for the large-scale analysis of phenotypic characteristics in wheat grains. Evaluation of our application under six different lighting conditions and three mobile devices demonstrated that the lighting of the paper has significant influence on the accuracy of our method, unlike the smartphone type.