Molecularly imprinted polymers: A closer look at the template removal and analyte binding.

Molecularly imprinted polymers (MIPs), which first appeared over half a century ago, are now attracting considerable attention as artificial receptors, particularly for sensing. MIPs, especially applied to biomedical analysis in biofluids, contribute significantly to patient diagnosis at the point of care, thereby allowing health monitoring. Despite the importance given to MIPs, removal of templates and binding of analytes have received little attention and are currently the least focused steps in MIP development.

Reconfigurable MIMO-based self-powered battery-less light communication system.

Simultaneous lightwave information and power transfer (SLIPT), co-existing with optical wireless communication, holds an enormous potential to provide continuous charging to remote Internet of Things (IoT) devices while ensuring connectivity. Combining SLIPT with an omnidirectional receiver, we can leverage a higher power budget while maintaining a stable connection, a major challenge for optical wireless communication systems. Here, we design a multiplexed SLIPT-based system comprising an array of photodetectors (PDs) arranged in a 3 × 3 configuration.

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.

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.

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.

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.

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/m 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.

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.

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.

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.

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 (NiMoO 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-NiMoO NRs.

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.

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.

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.

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.

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.

'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.

A highly selective electron affinity facilitated HS 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 HS selectively.

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.

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.

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.

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.

Unsupervised Adaptive Weight Pruning for Energy-Efficient Neuromorphic Systems.

To tackle real-world challenges, deep and complex neural networks are generally used with a massive number of parameters, which require large memory size, extensive computational operations, and high energy consumption in neuromorphic hardware systems. In this work, we propose an unsupervised online adaptive weight pruning method that dynamically removes non-critical weights from a spiking neural network (SNN) to reduce network complexity and improve energy efficiency. The adaptive pruning method explores neural dynamics and firing activity of SNNs and adapts the pruning threshold over time and neurons during training.

Laser scribed graphene: A novel platform for highly sensitive detection of electroactive biomolecules.

Laser-scribed graphene electrodes (LSGEs) have recently shown a potential for the development of electrochemical biosensors thanks to their electronic properties, porous structures, and large surface area that can support the charge transfer. In this paper, the authors present a comparative study of the electrochemical performances of LSGEs with the conventional screen-printed carbon electrodes (SPCEs) toward the detection of most commonly used phenolic compounds and biomolecules. Cyclic voltammetry measurements showed a significant enhancement in the electron transfer rate of all tested electroactive species at LSGEs compared to conventional SPCE.

Growth of large-scale MoS nanosheets on double layered ZnCoO for real-time HS monitoring in live cells.

There is an urgent need to develop in situ sensors that monitor the continued release of H2S from biological systems to understand H2S-related pathology and pharmacology. For this purpose, we have developed a molybdenum disulfide supported double-layered zinc cobaltite modified carbon cloth electrode (MoS2-ZnCo2O4-ZnCo2O4) based electrocatalytic sensor. The results of our study suggest that the MoS2-ZnCo2O4-ZnCo2O4 electrode has excellent electrocatalytic ability to oxidize H2S at physiological pH, in a minimized overpotential (+0.