As-designed Sensor Research Articles

A chemodosimetric chemosensor for the ratiometric detection of nerve agent-mimic DCP in solution and vapor phases.

Nerve agents are among the most deadly and lethal chemical warfare agents (CWAs). Rapid identification is crucial for specialized individuals to take action against dangerous drugs. This paper describes the synthesis and characterisation of a probe (MNFZ) based on the methoxy naphthalene-furoic hydrazide group. The probe rapidly (100 s) detects and quantifies the nerve-agent simulant diethyl chlorophosphate (DCP) in both solution and vapor phases. This sensor uses a new recognition center, furoic hydrazide, where the nitrogen atom of the imine group (CN) attacks the electrophilic core phosphorus atom of DCP, followed by the hydrolysis of the imine group in the acetonitrile (ACN) solution to produce the corresponding aldehyde MNPA. The development of ICT character resulted in a distinct red-shifted ratiometric fluorescence response to DCP, with a very low limit of detection (12.2 nM). The probe is an efficient chemosensor due to its high selectivity over other organophosphorus compounds as well as its chemical stability across a wide pH range. DFT calculations, 1H NMR and HRMS were performed to finalize the sensing mechanism. Lastly, the as-designed sensor was successfully used to build a highly sensitive portable kit in test strips and a cotton biopolymer for simple and safe real-time monitoring of DCP.

Monitoring antiviral active metabolite (N-hydroxycytidine) levels in plasma in presence of carboxylesterase-2 inhibitor verapamil using copper tetracyanoquinodimethane enhanced sensor

This study focuses on the determination of the active metabolite of molnupiravir (MOL), N-hydroxycytidine (NHC), using a square wave voltammetric (SWV) method. Carboxylesterase-2 enzymes catalyze the conversion of MOL prodrug into NHC. However, co-administration of verapamil (VER), a carboxylesterase-2 inhibitor, may reduce the levels of NHC, leading to decreasing its antiviral activity. In this context, the levels of NHC and VER were simultaneously monitored using a carbon paste electrode modified with phase I of copper tetracyanoquinodimethane (CuTCNQ) which is highly conductive charge transfer complex. The as-designed sensor was characterized successfully using various spectroscopic techniques and Scanning Electron Microscopy (SEM). The electrochemical behavior of the newly fabricated probe was examined using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). This method demonstrated its efficacy in measuring NHC and VER levels in rabbit plasma samples, showing high sensitivity and selectivity. The calibration plots for the simultaneous quantitation of NHC and VER displayed excellent linearity over the concentration ranges of 50–1600 nmol/L for NHC and 10–250 nmol/L for VER. The limits of detection (LOD) and quantitation (LOQ) in rabbit plasma were found to be 15.2 and 50.8 nmol/L for NHC and 2.9 and 9.9 nmol/L for VER, respectively. Moreover, fundamental pharmacokinetic parameters were calculated for NHC before and after co-administration of VER. The results suggest that the SWV method using CuTCNQ-modified CPE can be a useful tool for the determination of NHC and VER levels in plasma samples, with potential applications in the monitoring of drug-drug interactions involving carboxylesterase-2 inhibitors.

Terbium-based metal-organic frameworks through energy transfer modulation for visual logical sensing zinc and fluorine ions

Zinc is the second most abundant trace element in the human central nervous system, which is closely related to various physiological activities in the human body. Fluoride ion is one of the most harmful elements in drinking water. Excessive intake of F− may cause dental fluorosis, renal failure, or DNA damage. Therefore, it is urgent to develop sensors with high sensitivity and selectivity for the detection of Zn2+ and F− ions at the same time. In this work, a series of mixed lanthanide metal-organic frameworks (Ln-MOFs) probes are synthesized using a simple method of in situ doping. The luminous color can be finely modulated by changing the molar ratio of Tb3+ and Eu3+ during synthesis. Benefiting from the unique energy transfer modulation mechanism, the probe has the continuous detection capability of zinc ions and fluoride ions. The detection of Zn2+ and F− in a real environment shows that the probe has a good practical application prospect. The as-designed sensor at 262 nm excitation can sequentially detect Zn2+ concentrations ranging from 10−8 to 10−3 M (LOD = 4.2 nM) and F− levels ranging from 10−5 to 10−3 M (LOD = 3.6 μM) with high selectivity. Based on different output signals, a simple Boolean logic gate device is constructed to realize intelligent visualization of Zn2+ and F− monitoring.

A Wearable Respiration Sensor for Real-Time Monitoring of Chronic Kidney Disease.

Human respiration is accompanied with abundant physiological and pathological information, such as the change in ammonia (NH3) content, which is related to chronic kidney disease (CKD); hence, monitoring the breathing behavior helps in health assessment and illness prediction. In this work, a wearable respiration sensor based on CeO2@polyaniline (CeO2@PANI) nanocomposites that underwent a hydrogen plasma treatment is developed. The results unambiguously show that the response of the corresponding nanocomposites is significantly enhanced from 165 to 670% to 100 ppm NH3 compared to the counterpart that did not undergo hydrogen plasma treatment and even reaches 24% to 50 ppb NH3, suggesting its fascinating capability of detecting the trace level of NH3 in human breathing. The superior response for NH3 is ascribed to the stable oxygen vacancies produced by the hydrogen plasma treatment. Furthermore, the clinical tests for patients with uremia suggest that the as-designed sensor has potential applications in clinical monitoring for CKD. Herein, this work offers a new strategy to obtain respiration sensors with high performance and provides a feasible approach for health evaluation and disease monitoring of patients with CKD.

Outlier-Detection Methodology for Structural Identification Using Sparse Static Measurements.

The aim of structural identification is to provide accurate knowledge of the behaviour of existing structures. In most situations, finite-element models are updated using behaviour measurements and field observations. Error-domain model falsification (EDMF) is a multi-model approach that compares finite-element model predictions with sensor measurements while taking into account epistemic and stochastic uncertainties—including the systematic bias that is inherent in the assumptions behind structural models. Compared with alternative model-updating strategies such as residual minimization and traditional Bayesian methodologies, EDMF is easy-to-use for practising engineers and does not require precise knowledge of values for uncertainty correlations. However, wrong parameter identification and flawed extrapolation may result when undetected outliers occur in the dataset. Moreover, when datasets consist of a limited number of static measurements rather than continuous monitoring data, the existing signal-processing and statistics-based algorithms provide little support for outlier detection. This paper introduces a new model-population methodology for outlier detection that is based on the expected performance of the as-designed sensor network. Thus, suspicious measurements are identified even when few measurements, collected with a range of sensors, are available. The structural identification of a full-scale bridge in Exeter (UK) is used to demonstrate the applicability of the proposed methodology and to compare its performance with existing algorithms. The results show that outliers, capable of compromising EDMF accuracy, are detected. Moreover, a metric that separates the impact of powerful sensors from the effects of measurement outliers have been included in the framework. Finally, the impact of outlier occurrence on parameter identification and model extrapolation (for example, reserve capacity assessment) is evaluated.

ZnO particles enhanced graphene-based hybrid light sensors

Graphene with excellent mechanical properties shows potential applications in flexible devices. By decorating graphene with ZnO nanoparticles, the response of graphene-based flexible light sensor to light was enhanced. The light sensor showed different response rates with light intensities under different wavelength which indicated that the as-designed sensor can be used to detect the wavelength and intensity of light simultaneously. The microstructures and photonic properties of the hybrid device revealed that the interface played an important role on the performance of device. This result confirmed the method to prepare graphene/ZnO hybrid light sensor are convenient and economical, which may be applied in wearable devices.