Development and Application of Mobile Apps for Molecular Sensing: A Review

An artificial intelligence handheld sensor for direct reading of nickel ion and ethylenediaminetetraacetic acid in food samples using ratiometric fluorescence cellulose paper microfluidic chip

User-friendly in-field sensing protocol is crucial for the effective tracing of intended analytes under less-developed countries or resources-limited environments. Nevertheless, existing sensing strategies require professional technicians and expensive laboratory-based instrumentations, which are not capable for point-of-care on-site analyses. To address this issue, artificial intelligence handheld sensor has been designed for direct reading of Ni2+ and EDTA in food samples. The sensing platform incorporates smartphone with machine learning-driven application, 3D-printed handheld device, and cellulose paper microfluidic chip stained with ratiometric red-green-emission carbon dots (CDs). Intriguingly, Ni2+ introduction makes green fluorescent (FL) of CDs glow but red FL fade because of the coordination of Ni2+ with CDs verified by density functional theory (DFT), concurrently manifesting continuous FL colour transition from red to green. Subsequent addition of EDTA renders FL of CDs-Ni2+ recover owing to the capture of Ni2+ from CDs by EDTA based on strong chelation effect of EDTA on Ni2+ confirmed via DFT, accompanying with a noticeable colour returning from green to red. Inspired by above FL phenomena, CDs-based cellulose paper microfluidic chips are first fabricated to facilitate point-of-care testing of Ni2+ and EDTA. Designed fully-automatic handheld sensor is utilized to directly output Ni2+ and EDTA concentration in water, milk, spinach, bread, and shampoo based on wide linear ranges of 0-48 μM and 0-96 μM, and low limits of detection of 0.274 μM and 0.624 μM, respectively. The proposed protocol allows for speedy straightforward on-site determination of target analytes, which will trigger the development of automated and intelligent sensors in near future.

Role of pretty nanoflowers as novel versatile analytical tools for sensing in biomedical and bioanalytical applications

In recent years, an encouraging breakthrough in the synthesis of immobilized enzymes in flower-shaped called "organic-inorganic hybrid nanoflowers (hNFs)" with greatly enhanced catalytic activity and stability were reported. Although, these hNFs were discovered by accident, the enzymes exhibited highly enhanced catalytic activities and stabilities in the hNFs compared with the free and conventionally immobilized enzymes. Herein, we rationally utilized the catalytic activity of the hNFs for analytical applications. In this comprehensive review, we covered the design and use of the hNFs as novel versatile sensors for electrochemical, colorimetric/optical and immunosensors-based detection strategies in analytical perspective.

Artificial intelligence-integrated smartphone-based handheld detection of fluoride ion by Al3+-triggered aggregation-induced red-emssion enhanced carbon dots

Artificial intelligence (AI)-integrated smartphone-based handheld determination platform, based on 3D printed accessory, Al3+-triggered aggregation-induced red-emssion enhanced carbon dots (CDs) test strip, and smartphone with self-developed YOLO v3 AI algorithm-based application, proves the feasibility for intelligent real-time on-site quantitation of F- through tracking a consecutive fluorescence (FL) colour change. CDs, manifesting dual emission of moderate green emission at 512 nm and weak red one at 620 nm under 365 nm excitation, were synthesized hydrothermally from alizarin carmine and citric acid. CDs@Al3+, with distinct aggregation-induced red-emssion enhancement and green-emssion quenchment, were prepared by adding Al3+ to the CDs solution. Inspiringly, due to intrinsic ratiometric FL variation (I620/I512), CDs@Al3+ engender a successive FL colour variation from red to green in response to different concentrations of F- with low limit of detection of 7.998 μM and wide linear range of 150-1200 µM based on excellent linearity correlation between R/G value and F- concentration. Furthermore, F- content in tap water, toothpaste and milk could be intelligently, speedily, and straightforwardly analyzed through the AI-integrated smartphone-based handheld detection platform. It is fervently desired that our study will motivate a brand-new perspective for the promotion of efficacious detection strategy and the extension of practical application promise.

Electrochemical Affinity Biosensors: Pervasive Devices with Exciting Alliances and Horizons Ahead

Electrochemical affinity biosensors are evolving at breakneck speed, strengthening and colonizing more and more niches and drawing unimaginable roadmaps that increasingly make them protagonists of our daily lives. They achieve this by combining their intrinsic attributes with those acquired by leveraging the significant advances that occurred in (nano)materials technology, bio(nano)materials and nature-inspired receptors, gene editing and amplification technologies, and signal detection and processing techniques. The aim of this Perspective is to provide, with the support of recent representative and illustrative literature, an updated and critical view of the repertoire of opportunities, innovations, and applications offered by electrochemical affinity biosensors fueled by the key alliances indicated. In addition, the imminent challenges that these biodevices must face and the new directions in which they are envisioned as key players are discussed.

Glyphosate Separating and Sensing for Precision Agriculture and Environmental Protection in the Era of Smart Materials

The present article critically and comprehensively reviews the most recent reports on smart sensors for determining glyphosate (GLP), an active agent of GLP-based herbicides (GBHs) traditionally used in agriculture over the past decades. Commercialized in 1974, GBHs have now reached 350 million hectares of crops in over 140 countries with an annual turnover of 11 billion USD worldwide. However, rolling exploitation of GLP and GBHs in the last decades has led to environmental pollution, animal intoxication, bacterial resistance, and sustained occupational exposure of the herbicide of farm and companies' workers. Intoxication with these herbicides dysregulates the microbiome-gut-brain axis, cholinergic neurotransmission, and endocrine system, causing paralytic ileus, hyperkalemia, oliguria, pulmonary edema, and cardiogenic shock. Precision agriculture, i.e., an (information technology)-enhanced approach to crop management, including a site-specific determination of agrochemicals, derives from the benefits of smart materials (SMs), data science, and nanosensors. Those typically feature fluorescent molecularly imprinted polymers or immunochemical aptamer artificial receptors integrated with electrochemical transducers. Fabricated as portable or wearable lab-on-chips, smartphones, and soft robotics and connected with SM-based devices that provide machine learning algorithms and online databases, they integrate, process, analyze, and interpret massive amounts of spatiotemporal data in a user-friendly and decision-making manner. Exploited for the ultrasensitive determination of toxins, including GLP, they will become practical tools in farmlands and point-of-care testing. Expectedly, smart sensors can be used for personalized diagnostics, real-time water, food, soil, and air quality monitoring, site-specific herbicide management, and crop control.

A portable paper-based testing device for fast and on-site determination of nitroxynil in food

Nitroxynil (NTX) is a common anthelmintic veterinary drug for the management of fascioliasis in food-producing sheep and cattle. Since excessive NTX residue in food can lead to several adverse side effects, such as allergic skin reaction and respiratory irritation, it is of great importance to develop an efficient analytical method for NTX determination. Herein, we report a simple fluorescent detection method based on a novel supramolecular probe capable of detecting NTX with a fast response (5 s), high sensitivity (107 nM), high selectivity, and acceptable anti-interference property. Moreover, the portable paper-based test strips were facilely prepared and successfully realized on-site determination of NTX in real edible animal products simply with the aid of a smartphone. To the best of our knowledge, this is the very first report on the portable detection of NTX. This study also provides a promising strategy for the fast and portable detection of analyte based on the host-guest system, which will lead to improved fluorescent probe design for food analysis.

A smartphone-assisted optosensing platform based on chromium-based metal-organic framework signal amplification for ultrasensitive and real-time determination of oxytetracycline

Ultrasensitive and onsite detection of oxytetracycline (OTC) is of vital significance for ensuring public health. Herein, a novel and versatile fluorescence biomimetic nanosensor, Mg,N-CDs@MIL-101@MIP, was elaborately tailored for the assay of OTC. MIL-101 with extraordinarily high surface area and porosity, as a favorable supporter, suppressed self-quenching of Mg,N-CDs and boosted mass transfer rate, realizing signal amplification. As an ultrasensitive signal transducer, high luminescent Mg,N-CDs yielded conspicuous fluorescence responses for OTC, enhancing the sensitivity of Mg,N-CDs@MIL-101@MIP. High-affinity imprinting sites further endowed Mg,N-CDs@MIL-101@MIP with superior anti-interference ability and reusability. Given prominent merits, Mg,N-CDs@MIL-101@MIP demonstrated a good linear range (0.05-40 μg mL^-1) with a lower limit of detection (16.8 ng mL^-1), supplying high accessibility to realize ultrasensitive and highly selective measurement of OTC in samples. Additionally, to attain precise onsite profiling of OTC, an intelligent sensing platform was developed by integrating Mg,N-CDs@MIL-101@MIP with a portable smartphone-assisted optical device. As both signal reader and analyzer, smartphone can instantly capture concentration-dependent fluorescent images and accurately digitize them, accomplishing quantitative analysis of OTC. More delightfully, the portable platform was utilized for visual determination of OTC in milk samples with satisfactory results, offering a promising tool for the high-performance onsite evaluation of food safety and environmental health.

A Comprehensive Study: Traditional and Cutting-Edge Analytical Techniques for the Biomarker Based Detection of the Micronutrients & POC Sensing Directions for Next-Generation Diagnostic

Micronutrient deficiency is wide spread and highly affects morbidity, mortality, and well-being of human beings. Micronutrient deficiency gradually manifests into diseases, which effects pathophysiology directly or indirectly. There is an imprecision in the diagnosis of micronutrient deficiency because of two causes; the selection of the standard biomarker and the diagnostic technique used. In appropriate diagnosis could increase the severity of the disorder. Instead of a single a combination of biomarkers can give more stringent results for micronutrient testing. Several traditional analytical techniques are used for diagnosis but HPLC, ELISA & LCMS/MS are most sensitive and reliable methods used by CLSIA-certified labs. However, these techniques require well-equipped, centralized laboratory facilities. The diagnostic era moves toward the Point of Care Testing (POCT), a boon in emerging diagnostics, breaking all paradigms of traditional analytical techniques. POCT led us toward the development of biosensors, which encompasses many techniques like paper-based sensors, microfluidic chip, wearable devices, and smartphone-assisted diagnostics, which become more popular diagnostic tools. This outlook summarizes the micronutrients like vitamins A, B5, B6, B7, B9, B12 C, D, and E and Minerals like iron, calcium, zinc, magnesium, and sodium; along with its biomarkers, analytical techniques, and point of care innovation in micronutrients.

Simultaneous Cross-type Detection of Water Quality Indexes via a Smartphone-App Integrated Microfluidic Paper-Based Platform

Water quality guarantee in remote areas necessitates the development of portable, sensitive, fast, cost-effective, and easy-to-use water quality detection methods. The current work reports on a microfluidic paper-based analytical device (μPAD) integrated with a smartphone app for the simultaneous detection of cross-type water quality parameters including pH, Cu(II), Ni(II), Fe(III), and nitrite. The shapes, baking time, amount, and ratios of reaction reagent mixtures of wax μPAD were optimized to improve the color uniformity and intensity effectively. An easy-to-use smartphone app was established for recording, analyzing, and directly reading the colorimetric signals and target concentrations on μPAD. The results showed that under the optimum conditions, the current analytical platform has reached the detection limits of 0.4, 1.9, 2.9, and 1.1 ppm for nitrite, Cu(II), Ni(II), and Fe(III), respectively, and the liner ranges are 2.3-90 ppm (nitrite), 3.8-400 ppm (Cu(II)), 2.9-1000 ppm (Ni(II)), 2.8-500 ppm (Fe(III)), and 5-9 (pH). The proposed portable smartphone-app integrated μPAD detection system was successfully applied to real industrial wastewater and river water quality monitoring. The proposed method has great potential for field water quality detection.

Promising instrument-free detections of various analytes using smartphones with Spotxel® Reader

In consideration of the problems related to food safety, environmental pollution, and the spread of infected diseases nowadays, we urgently need testing methods that can be easily performed by common people. Smartphone-based detections are promising for general applications. However, some of these analytical strategies require a combination of accessories and instruments, such as portable electrochemical workstations, mini multi-mode microplate readers, and complex temperature control devices, etc., which are small but still expensive. Herein, we comprehensively introduce a free app (Spotxel^® Reader) that can provide accurate data analysis for microplate or parallel-format test sensors without an instrument. By simulating the optical signal of the test samples through a smartphone, the sensing results can be obtained for free. We discuss the detection strategies involved in the reported smartphone-based analyses using Spotxel^® Reader. Prospects for the development of this free app for future detection applications are presented. This review aims to popularize free analysis software, so that ordinary people may realize convenient tests.

Multiplexed Lab-on-a-Chip Bioassays for Testing Antibodies against SARS-CoV-2 and Its Variants in Multiple Individuals

Neutralization assays that can measure neutralizing antibodies in serum are vital for large-scale serodiagnosis and vaccine evaluation. Here, we establish multiplexed lab-on-a-chip bioassays for testing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. Compared with enzyme-linked immunosorbent assay (ELISA), our method exhibits a low consumption of sample and reagents (10 μL), a low limit of detection (LOD: 0.08 ng/mL), a quick sample-to-answer time (about 70 min), and multiplexed ability (5 targets in each of 7 samples in one assay). We can also increase the throughput as needed. The concentrations of antibodies against RBD, D614G, N501Y, E484K, and L452R/E484Q-mutants after two doses of vaccines are 6.6 ± 3.6, 8.7 ± 4.6, 3.4 ± 2.8, 3.8 ± 2.8, and 2.8 ± 2.3 ng/mL, respectively. This suggests that neutralizing activities against N501Y, E484K, and L452R/E484Q-mutants were less effective than RBD and D614G-mutant. We performed a plaque reduction neutralization test (PRNT) for all volunteers. Compared with PRNT, our assay is fast, accurate, inexpensive, and multiplexed with multiple-sample processing ability, which is good for large-scale serodiagnosis and vaccine evaluation.

A smartphone-based device for simultaneous measurement of ratiometric fluorescence and absorbance demonstrated by the determination of hypochlorous acid

Ratiometric fluorescence and absorbance are measured simultaneously by a smartphone-based device according to the corrected brightness ratio in a picture.

Combining Electrochemiluminescence Detection with Aptamer-Gated Indicator Releasing Mesoporous Nanoparticles Enables ppt Sensitivity for Strip-Based Rapid Tests

The combination of electrogenerated chemiluminescence (ECL) and aptamer-gated indicator delivering (gAID) magnetic mesoporous silica nanoparticles embedded into glass fibre paper functionalised with poly(ethyleneglycol) and N-(3-triethoxysilylpropyl)diethanolamine allowed the development of a rapid test that detects penicillin directly in diluted milk down to 50±9 ppt in <5 min. Covalent attachment of the aptamer "cap" to the silica scaffold enabled pore closure through non-covalent electrostatic interactions with surface amino groups, while binding of penicillin led to a folding-up of the aptamer thus releasing the ECL reporter Ru(bpy)32+ previously loaded into the material and letting it be detected after lateral flow by a smartphone camera upon electrochemical excitation with a screen printed electrode inserted into a 3D-printed holder. The approach is simple, generic and presents advantages with respect to sensitivity, measurement uncertainty and robustness compared with conventional fluorescence or electrochemical detection, especially for point-of-need analyses of challenging matrices and analytes at ultra-trace levels.

Non-enzymatic colorimetric detection of hydrogen peroxide using a μPAD coupled with a machine learning-based smartphone app

In the present study, iodide-mediated 3,3',5,5'-tetramethylbenzidine (TMB)-H₂O₂ reaction system was applied to a microfluidic paper-based analytical device (μPAD) for non-enzymatic colorimetric determination of H₂O₂. The proposed system is portable and incorporates a μPAD with a machine learning-based smartphone app. A smartphone app called "Hi-perox Sens" capable of image capture, cropping and processing was developed to make the system simple and user-friendly. Briefly, circular μPADs were designed and tested with varying concentrations of H₂O₂. Following the color change, the images of the μPADs were taken with four different smartphones under seven different illumination conditions. In order to make the system more robust and adaptive against illumination variation and camera optics, the images were first processed for feature extraction and then used to train machine learning classifiers. According to the results, TMB + KI showed the highest classification accuracy (97.8%) with inter-phone repeatability at t = 30 s under versatile illumination and maintained its accuracy for 10 minutes. In addition, the performance of the system was also comparable to two different commercially available H₂O₂ kits in real samples.