Smart Multiplex Point-of-Care Platform for Simultaneous Drug Monitoring.

Recently, illicit drug use has become more widespread and is linked to problems with crime and public health. These drugs disrupt consciousness, affecting perceptions and feelings. Combining stimulants and depressants to suppress the effect of drugs has become the most common reason for drug overdose deaths. On-site platforms for illicit-drug detection have gained an important role in dealing, without any excess equipment, long process, and training, with drug abuse and drug trafficking. Consequently, the development of rapid, sensitive, noninvasive, and reliable multiplex drug-detecting platforms has become a major necessity. In this study, a multiplex laser-scribed graphene (LSG) sensing platform with one counter, one reference, and three working electrodes was developed for rapid and sensitive electrochemical detection of amphetamine (AMP), cocaine (COC), and benzodiazepine (BZD) simultaneously in saliva samples. The multidetection sensing system was combined with a custom-made potentiostat to achieve a complete point-of-care (POC) platform. Smartphone integration was achieved by a customized application to operate, display, and send data. To the best of our knowledge, this is the first multiplex LSG-based electrochemical platform designed for illicit-drug detection with a custom-made potentiostat device to build a complete POC platform. Each working electrode was optimized with standard solutions of AMP, COC, and BZD in the concentration range of 1.0 pg/mL-500 ng/mL. The detection limit of each illicit drug was calculated as 4.3 ng/mL for AMP, 9.7 ng/mL for BZD, and 9.0 ng/mL for COC. Healthy and MET (methamphetamine) patient saliva samples were used for the clinical study. The multiplex LSG sensor was able to detect target analytes in real saliva samples successfully. This multiplex detection device serves the role of a practical and affordable alternative to conventional drug-detection methods by combining multiple drug detections in one portable platform.

Efficient training of spiking neural networks with temporally-truncated local backpropagation through time.

Directly training spiking neural networks (SNNs) has remained challenging due to complex neural dynamics and intrinsic non-differentiability in firing functions. The well-known backpropagation through time (BPTT) algorithm proposed to train SNNs suffers from large memory footprint and prohibits backward and update unlocking, making it impossible to exploit the potential of locally-supervised training methods. This work proposes an efficient and direct training algorithm for SNNs that integrates a locally-supervised training method with a temporally-truncated BPTT algorithm. The proposed algorithm explores both temporal and spatial locality in BPTT and contributes to significant reduction in computational cost including GPU memory utilization, main memory access and arithmetic operations. We thoroughly explore the design space concerning temporal truncation length and local training block size and benchmark their impact on classification accuracy of different networks running different types of tasks. The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on event-based datasets. In spite of resulting information loss, local training is capable of alleviating overfitting. The combined effect of temporal truncation and local training can lead to the slowdown of accuracy drop and even improvement in accuracy. In addition, training deep SNNs' models such as AlexNet classifying CIFAR10-DVS dataset leads to 7.26% increase in accuracy, 89.94% reduction in GPU memory, 10.79% reduction in memory access, and 99.64% reduction in MAC operations compared to the standard end-to-end BPTT. Thus, the proposed method has shown high potential to enable fast and energy-efficient on-chip training for real-time learning at the edge.

An interconnect-free micro-electromechanical 7-bit arithmetic device for multi-operand programmable computing.

Computational power density and interconnection between transistors have grown to be the dominant challenges for the continued scaling of complementary metal-oxide-semiconductor (CMOS) technology due to limited integration density and computing power. Herein, we designed a novel, hardware-efficient, interconnect-free microelectromechanical 7:3 compressor using three microbeam resonators. Each resonator is configured with seven equal-weighted inputs and multiple driven frequencies, thus defining the transformation rules for transmitting resonance frequency to binary outputs, performing summation operations, and displaying outputs in compact binary format. The device achieves low power consumption and excellent switching reliability even after 3 × 103 repeated cycles. These performance improvements, including enhanced computational power capacity and hardware efficiency, are paramount for moderately downscaling devices. Finally, our proposed paradigm shift for circuit design provides an attractive alternative to traditional electronic digital computing and paves the way for multioperand programmable computing based on electromechanical systems.

Polyoxometalate-cyclodextrin supramolecular entities for real-time in situ monitoring of dopamine released from neuroblastoma cells.

Optimized and sensitive biomarker detection has recently been shown to have a critical impact on quality of diagnosis and medical care options. In this research study, polyoxometalate-γ-cyclodextrin metal-organic framework (POM-γCD MOF) was utilized as an electrocatalyst to fabricate highly selective sensors to detect in-situ released dopamine. The POM-γCD MOF produced multiple modes of signals for dopamine including electrochemical, colorimetric, and smartphone read-outs. Real-time quantitative monitoring of SH-SY5Y neuroblastoma cellular dopamine production was successfully demonstrated under various stimuli at different time intervals. The POM-CD MOF sensor and linear regression model were used to develop a smartphone read-out platform, which converts dopamine visual signals to digital signals within a few seconds. Ultimately, POM-γCD MOFs can play a significant role in the diagnosis and treatment of various diseases that involve dopamine as a significant biomarker.

Effect of combining UV-C irradiation and vacuum sealing on the shelf life of fresh strawberries and tomatoes.

This research presents the effect of combining UV-C irradiation and vacuum sealing on the shelf life of strawberries and quartered tomatoes and compares it with the effect of the sole use of UV-C irradiation or vacuum sealing. A constant UV-C dose of 360 J/m2 was used for the samples' irradiation, and all the vacuum-sealed samples were stored at a reduced pressure of 40 kPa. Organoleptic analysis, microbial population quantification of yeast and mold, Pseudomonas sp., weight loss, and pH measurements were obtained to identify the spoilage occurrence, monitor the samples' quality, and quantify the shelf life. Sensory evaluation was conducted by 12 consumer panelists to evaluate the aroma, taste, color, texture, and the overall acceptance of the samples. The results revealed that the combination of UV-C irradiation and vacuum sealing prolongs the shelf life of perishables more than the sole use of UV-C irradiation or vacuum sealing. The achieved shelf-life increase using this combination was 124.41% and 54.41% for strawberries and quartered tomatoes, respectively, while acceptable sensory characteristics were maintained throughout the storage period. Hence, this food preservation method can be further improved and integrated in the daily life of modern consumers and the operations of fresh produce retailers, as it could effectively reduce the spoilage rates of fresh produce and help achieve the UN SDG 12.3, which aims to reduce food loss and waste by 50% by 2030 at the consumer and retail levels. PRACTICAL APPLICATION: The system can be further developed and introduced to the market as a kitchen appliance for households or as a predistribution step for fresh produce distribution centers. The shelf-life extension capability of this system, which does not involve any use of chemical substances, would make it an attractive solution for households and food retailers.

Experimental Identification of the Second-Order Non-Hermitian Skin Effect with Physics-Graph-Informed Machine Learning.

Topological phases of matter are conventionally characterized by the bulk-boundary correspondence in Hermitian systems. The topological invariant of the bulk in d dimensions corresponds to the number of (d - 1)-dimensional boundary states. By extension, higher-order topological insulators reveal a bulk-edge-corner correspondence, such that nth order topological phases feature (d - n)-dimensional boundary states. The advent of non-Hermitian topological systems sheds new light on the emergence of the non-Hermitian skin effect (NHSE) with an extensive number of boundary modes under open boundary conditions. Still, the higher-order NHSE remains largely unexplored, particularly in the experiment. An unsupervised approach-physics-graph-informed machine learning (PGIML)-to enhance the data mining ability of machine learning with limited domain knowledge is introduced. Through PGIML, the second-order NHSE in a 2D non-Hermitian topoelectrical circuit is experimentally demonstrated. The admittance spectra of the circuit exhibit an extensive number of corner skin modes and extreme sensitivity of the spectral flow to the boundary conditions. The violation of the conventional bulk-boundary correspondence in the second-order NHSE implies that modification of the topological band theory is inevitable in higher dimensional non-Hermitian systems.

Metal-Organic Frameworks Meet Molecularly Imprinted Polymers: Insights and Prospects for Sensor Applications.

The use of porous materials as the core for synthesizing molecularly imprinted polymers (MIPs) adds significant value to the resulting sensing system. This review covers in detail the current progress and achievements regarding the synergistic combination of MIPs and porous materials, namely metal/covalent-organic frameworks (MOFs/COFs), including the application of such frameworks in the development of upgraded sensor platforms. The different processes involved in the synthesis of MOF/COF-MIPs are outlined, along with their intrinsic properties. Special attention is paid to debriefing the impact of the morphological changes that occur through the synergistic combination compared to those that occur due to the individual entities. Thereafter, the strategies used for building the sensors, as well as the transduction modes, are overviewed and discussed. This is followed by a full description of research advances for various types of MOF/COF-MIP-based (bio)sensors and their applications in the fields of environmental monitoring, food safety, and pharmaceutical analysis. Finally, the challenges/drawbacks, as well as the prospects of this research field, are discussed in detail.

Ruthenium and Nickel Molybdate-Decorated 2D Porous Graphitic Carbon Nitrides for Highly Sensitive Cardiac Troponin Biosensor.

Two-dimensional (2D) layered materials functionalized with monometallic or bimetallic dopants are excellent materials to fabricate clinically useful biosensors. Herein, we report the synthesis of ruthenium nanoparticles (RuNPs) and nickel molybdate nanorods (NiMoO4 NRs) functionalized porous graphitic carbon nitrides (PCN) for the fabrication of sensitive and selective biosensors for cardiac troponin I (cTn-I). A wet chemical synthesis route was designed to synthesize PCN-RuNPs and PCN-NiMoO4 NRs. Morphological, elemental, spectroscopic, and electrochemical investigations confirmed the successful formation of these materials. PCN-RuNPs and PCN-NiMoO4 NRs interfaces showed significantly enhanced electrochemically active surface areas, abundant sites for immobilizing bioreceptors, porosity, and excellent aptamer capturing capacity. Both PCN-RuNPs and PCN-NiMoO4 NRs materials were used to develop cTn-I sensitive biosensors, which showed a working range of 0.1-10,000 ng/mL and LODs of 70.0 pg/mL and 50.0 pg/mL, respectively. In addition, the biosensors were highly selective and practically applicable. The functionalized 2D PCN materials are thus potential candidates to develop biosensors for detecting acute myocardial infractions.

Graphitic Carbon Nitride and IGZO Bio-FET for Rapid Diagnosis of Myocardial Infarction.

Acute myocardial infarction (AMI), commonly known as a heart attack, is a life-threatening condition that causes millions of deaths every year. In this study, a transistor-based biosensor is developed for rapid and sensitive detection of cardiac troponin-I (cTnI), a diagnostic biomarker of AMI. A biosensing technique based on a field effect transistor (FET), which uses indium gallium zinc oxide (IGZO) as an excellent semiconducting channel, is integrated with nanosheet materials to detect cTnI. Porous carbon nitride (PCN) decorated with gold nanoparticles (Au NPs) is used as a bridge between the solid-state device and the biorecognition element. We demonstrate that this biosensor is highly sensitive and has an experimental limit of detection of 0.0066 ng/mL and a dynamic range of 0.01 ng/mL-1000 ng/mL. This is the first report of a semiconducting metal oxide FET cardiac biomarker sensor combined with PCN for the detection of cTnI. The reported compact microsystem paves the way for rapid and inexpensive detection of cardiac biomarkers.

Multiplexed sensing techniques for cardiovascular disease biomarkers - A review.

Cardiovascular diseases (CVDs) are the number one cause of death worldwide, taking 17.9 million lives each year. The rapid, sensitive, and accurate determination of cardiac biomarkers is vital for the timely diagnosis of CVDs. For accurate diagnosis, dependence on a single biomarker is unreliable because each one has also been linked to other diseases. To overcome this problem, the multiplexed determination of two or more markers has emerged as a promising alternative to single-marker analysis. Over the last 5 years, research interest in the development of biosensors for targeting multiple cardiac markers has increased. In this study, we critically reviewed the various multiplexed biosensing approaches reported during the last 5 years, categorizing them by signal readouts. Prospective detection configurations, capture probes, electrode design strategies, electrode types, nanomaterials, reporter tags, and assay types were reviewed, tabulated, and critically discussed. Then, their advantages and limitations were highlighted. For each category, we provided our perspective as well as the overall critical discussion. Lastly, we summarized potential commercial multiplexed cardiac biosensors and commented on the challenges and future prospects for such sensors.

Smartphone-Based Multiplexed Biosensing Tools for Health Monitoring.

Many emerging technologies have the potential to improve health care by providing more personalized approaches or early diagnostic methods. In this review, we cover smartphone-based multiplexed sensors as affordable and portable sensing platforms for point-of-care devices. Multiplexing has been gaining attention recently for clinical diagnosis considering certain diseases require analysis of complex biological networks instead of single-marker analysis. Smartphones offer tremendous possibilities for on-site detection analysis due to their portability, high accessibility, fast sample processing, and robust imaging capabilities. Straightforward digital analysis and convenient user interfaces support networked health care systems and individualized health monitoring. Detailed biomarker profiling provides fast and accurate analysis for disease diagnosis for limited sample volume collection. Here, multiplexed smartphone-based assays with optical and electrochemical components are covered. Possible wireless or wired communication actuators and portable and wearable sensing integration for various sensing applications are discussed. The crucial features and the weaknesses of these devices are critically evaluated.

Minimally-invasive, real-time, non-destructive, species-independent phytohormone biosensor for precision farming.

To keep up with population growth, precision farming technologies must be implemented to sustainably increase agricultural output. The impact of such technologies can be expanded by monitoring phytohormones, such as salicylic acid. In this study, we present a plant-wearable electrochemical sensor for in situ detection of salicylic acid. The sensor utilizes microneedle-based electrodes that are functionalized with a layer of salicylic acid selective magnetic molecularly imprinted polymers. The sensor's capability to detect the phytohormone is demonstrated both in vitro and in vivo with a limit of detection of 2.74 µM and a range of detection that can reach as high as 150 µM. Furthermore, the selectivity of the sensor is verified by testing the sensor on commonly occurring phytohormones. Finally, we demonstrate the capability of the sensor to detect the onset of fungal infestation in Tobacco 5 min post-inoculation. This work shows that the sensor could serve as a promising platform for continuous and non-destructive monitoring in the field and as a fundamental research tool when coupled with a portable potentiostat.

A Portable Molecularly Imprinted Sensor for On-Site and Wireless Environmental Bisphenol A Monitoring.

The detection of pollutant traces in the public and environmental waters is essential for safety of the population. Bisphenol A (BPA) is a toxic chemical widely used for the production of food storage containers by plastic industries to increase the storage ability. However, the insertion of BPA in water medium leads to serious health risks. Therefore, the development of low-cost, practical, sensitive, and selective devices to monitor BPA levels on-site in the environment is highly needed. Herein, for the first time, we present a homemade portable potentiostat device integrated to a laser-scribed graphene (LSG) sensor for BPA detection as a practical environmental pollutant monitoring tool. Recently, there has been an increasing need regarding the development of graphene-based electrochemical transducers (e.g., electrodes) to obtain efficient biosensing platforms. LSG platform is combined with molecularly imprinted polymer (MIP) matrix. LSG electrodes were modified with gold nanostructures and PEDOT polymer electrodeposition to create a specific MIP biomimetic receptor for ultrasensitive BPA detection. The sensing device has a Bluetooth connection, wirelessly connected to a smartphone providing high sensitivity and sensitivity (LOD: 3.97 nM in a linear range of .01-10 µM) toward BPA. Two commercial bottled water samples, tap water, commercial milk, and baby formula samples have been used to validate the reliability of the portable sensor device.

Self-powered weather station for remote areas and difficult-access locations.

Monitoring climate change can be accomplished by deploying Internet of Things (IoT) sensor devices to collect data on various climate variables. Providing continuous power or replacing batteries for these devices is not always available, particularly in difficult-access locations and harsh environments. Here, we propose a design for a self-powered weather station that can harvest energy, decode information using solar cells, and is controlled by a programmable system-on-chip. A series of experimental demonstrations have shown the versatility of the proposed design to operate autonomously.

'All In One' SARS-CoV-2 variant recognition platform: Machine learning-enabled point of care diagnostics.

Point of care (PoC) devices are highly demanding to control current pandemic, originated from severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Though nucleic acid-based methods such as RT-PCR are widely available, they require sample preparation and long processing time. PoC diagnostic devices provide relatively faster and stable results. However they require further investigation to provide high accuracy and be adaptable for the new variants. In this study, laser-scribed graphene (LSG) sensors are coupled with gold nanoparticles (AuNPs) as stable promising biosensing platforms. Angiotensin Converting Enzyme 2 (ACE2), an enzymatic receptor, is chosen to be the biorecognition unit due to its high binding affinity towards spike proteins as a key-lock model. The sensor was integrated to a homemade and portable potentistat device, wirelessly connected to a smartphone having a customized application for easy operation. LODs of 5.14 and 2.09 ng/mL was achieved for S1 and S2 protein in the linear range of 1.0-200 ng/mL, respectively. Clinical study has been conducted with nasopharyngeal swabs from 63 patients having alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2) variants, patients without mutation and negative patients. A machine learning model was developed with accuracy of 99.37% for the identification of the SARS-Cov-2 variants under 1 min. With the increasing need for rapid and improved disease diagnosis and monitoring, the PoC platform proved its potential for real time monitoring by providing accurate and fast variant identification without any expertise and pre sample preparation, which is exactly what societies need in this time of pandemic.

Toward the Optimal Design and FPGA Implementation of Spiking Neural Networks.

The performance of a biologically plausible spiking neural network (SNN) largely depends on the model parameters and neural dynamics. This article proposes a parameter optimization scheme for improving the performance of a biologically plausible SNN and a parallel on-field-programmable gate array (FPGA) online learning neuromorphic platform for the digital implementation based on two numerical methods, namely, the Euler and third-order Runge-Kutta (RK3) methods. The optimization scheme explores the impact of biological time constants on information transmission in the SNN and improves the convergence rate of the SNN on digit recognition with a suitable choice of the time constants. The parallel digital implementation leads to a significant speedup over software simulation on a general-purpose CPU. The parallel implementation with the Euler method enables around 180× ( 20× ) training (inference) speedup over a Pytorch-based SNN simulation on CPU. Moreover, compared with previous work, our parallel implementation shows more than 300× ( 240× ) improvement on speed and 180× ( 250× ) reduction in energy consumption for training (inference). In addition, due to the high-order accuracy, the RK3 method is demonstrated to gain 2× training speedup over the Euler method, which makes it suitable for online training in real-time applications.

A highly selective electron affinity facilitated H2S sensor: the marriage of tris(keto-hydrazone) and an organic field-effect transistor.

Conjugated polymers (CPs) are emerging as part of a promising future for gas-sensing applications. However, some of their limitations, such as poor specificity, humidity sensitivity and poor ambient stability, remain persistent. Herein, a novel combination of a polymer-monomer heterostructure, derived from a CP (PDVT-10) and a newly reported monomer [tris(keto-hydrazone)] has been integrated in an organic field-effect transistor (OFET) platform to sense H2S selectively. The hybrid heterostructure shows an unprecedented sensitivity (525% ppm-1) and high selectivity toward H2S gas. In addition, we demonstrated that the PDVT-10/tris(keto-hydrazone) OFET sensor has the lowest limit of detection (1 ppb), excellent ambient stability (∼5% current degradation after 150 days), good response-recovery behavior, and exceptional electrical behavior and gas response reproducibility. This work can help pave the way to incorporate futuristic gas sensors in a multitude of applications.

Robust, Long-Term, and Exceptionally Sensitive Microneedle-Based Bioimpedance Sensor for Precision Farming.

Precision farming has the potential to increase global food production capacity whilst minimizing traditional inputs. However, the adoption and impact of precision farming are contingent on the availability of sensors that can discern the state of crops, while not interfering with their growth. Electrical impedance spectroscopy offers an avenue for nondestructive monitoring of crops. To that end, it is reported on the deployment of impedimetric sensors utilizing microneedles (MNs) that can be used to pierce the waxy exterior of plants to obtain sensitive impedance spectra in open-air settings with an average relative noise value of 3.83%. The sensors are fabricated using a novel micromolding and release method that is compatible with UV photocurable and thermosetting polymers. Assessments of the quality of the MNs under scanning electron microscopy show that the replication process is high in fidelity to the original design of the master mold and that it can be used for upward of 20 replication cycles. The sensor's performance is validated against conventional planar sensors for obtaining the impedance values of Arabidopsis thaliana. As a change is detected in impedance due to lighting and hydration, this raises the possibility for their widespread use in precision farming.

Neural Coding in Spiking Neural Networks: A Comparative Study for Robust Neuromorphic Systems.

Various hypotheses of information representation in brain, referred to as neural codes, have been proposed to explain the information transmission between neurons. Neural coding plays an essential role in enabling the brain-inspired spiking neural networks (SNNs) to perform different tasks. To search for the best coding scheme, we performed an extensive comparative study on the impact and performance of four important neural coding schemes, namely, rate coding, time-to-first spike (TTFS) coding, phase coding, and burst coding. The comparative study was carried out using a biological 2-layer SNN trained with an unsupervised spike-timing-dependent plasticity (STDP) algorithm. Various aspects of network performance were considered, including classification accuracy, processing latency, synaptic operations (SOPs), hardware implementation, network compression efficacy, input and synaptic noise resilience, and synaptic fault tolerance. The classification tasks on Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets were applied in our study. For hardware implementation, area and power consumption were estimated for these coding schemes, and the network compression efficacy was analyzed using pruning and quantization techniques. Different types of input noise and noise variations in the datasets were considered and applied. Furthermore, the robustness of each coding scheme to the non-ideality-induced synaptic noise and fault in analog neuromorphic systems was studied and compared. Our results show that TTFS coding is the best choice in achieving the highest computational performance with very low hardware implementation overhead. TTFS coding requires 4x/7.5x lower processing latency and 3.5x/6.5x fewer SOPs than rate coding during the training/inference process. Phase coding is the most resilient scheme to input noise. Burst coding offers the highest network compression efficacy and the best overall robustness to hardware non-idealities for both training and inference processes. The study presented in this paper reveals the design space created by the choice of each coding scheme, allowing designers to frame each scheme in terms of its strength and weakness given a designs' constraints and considerations in neuromorphic systems.

Theory and experimental verification of configurable computing with stochastic memristors.

The inevitable variability within electronic devices causes strict constraints on operation, reliability and scalability of the circuit design. However, when a compromise arises among the different performance metrics, area, time and energy, variability then loosens the tight requirements and allows for further savings in an alternative design scope. To that end, unconventional computing approaches are revived in the form of approximate computing, particularly tuned for resource-constrained mobile computing. In this paper, a proof-of-concept of the approximate computing paradigm using memristors is demonstrated. Stochastic memristors are used as the main building block of probabilistic logic gates. As will be shown in this paper, the stochasticity of memristors' switching characteristics is tightly bound to the supply voltage and hence to power consumption. By scaling of the supply voltage to appropriate levels stochasticity gets increased. In order to guide the design process of approximate circuits based on memristors a realistic device model needs to be elaborated with explicit emphasis of the probabilistic switching behavior. Theoretical formulation, probabilistic analysis, and simulation of the underlying logic circuits and operations are introduced. Moreover, the expected output behavior is verified with the experimental measurements of valence change memory cells. Hence, it is shown how the precision of the output is varied for the sake of the attainable gains at different levels of available design metrics. This approach represents the first proposition along with physical verification and mapping to real devices that combines stochastic memristors into unconventional computing approaches.