Search Results (692)

Current–voltage (I–V) characteristics are an important measure of photovoltaic (PV) generators, corresponding to environmental conditions regarding solar irradiance and temperature. The I–V curve tracer is a widely used instrument in power engineering to evaluate system performance and detect fault conditions in PV power systems. Several technologies have been applied to develop the device and trace I–V characteristics, improving accuracy, speed, and portability. Focusing on the outdoor environment, this paper presents an in-depth analysis and comparison of the system design and dynamics to identify the I–V tracing performance based on different power conversion topologies and data acquisition methods. This is a valuable reference for industry and academia to further the technology and promote sustainable power generation. Full article

Hypochlorous acid (HClO) serves as a biological mediator and is widely utilized as a disinfectant in food processing and water treatment. However, excessive HClO residues in food and environmental water raise concerns due to the potential formation of carcinogenic chlorinated byproducts and disinfection byproducts (DBPs). Despite its importance, traditional methods for HClO detection often involve complex sample preparation, sophisticated instrumentation, and skilled operators. Herein, we report an aggregation-induced emission (AIE) small molecule fluorescent probe (NYV) that integrates colorimetric and ratiometric fluorescence responses for the detection of HClO. This probe exhibits high sensitivity, with a detection limit of 0.35 $\mathsf{\mu }$M, a rapid response time of 1 min, and a wide linear range (0–142.5 $\mathsf{\mu }$M), along with anti-interference capabilities, making it suitable for real-time monitoring. Furthermore, we have developed a portable solid-state sensor based on probe NYV for the rapid visual detection of HClO. The potential applications of this probe in real sample analysis and bioimaging experiments are demonstrated. Our findings contribute to the development of innovative fluorescent probes for HClO detection, with broad applications in food safety, environmental monitoring, and biomedical research on oxidative stress and ferroptosis. Full article

Atmospheric optical turbulence intensity, quantified by the Fried parameter ($r_0$), serves as a critical metric for astronomical site testing and selection. The Solar Differential Image Motion Monitor (SDIMM), adapted from the methodology of the Differential Image Motion Monitor (DIMM), is a dedicated instrument for daytime $r_0$ measurements. Conventional SDIMM systems typically employ telescopes with apertures ≥30 cm and reconstruct wavefront segmentation at the exit pupil, resulting in bulky configurations that impede portability. To address the demands of multi-site surveys, we developed a low-cost, portable SDIMM system that directly adopts the DIMM optical path without backend wavefront reconstruction, instead deriving $r_0$ through image processing algorithms. Integrated with a 20 cm aperture telescope, the system achieves a total weight of <20 kg, significantly enhancing field portability. This paper details the instrument’s architecture, measurement principles, and comparative tests with a traditional SDIMM, demonstrating strong consistency between the two systems. Field measurements conducted at the Ali Observatory (elevation: 5050 m) from 16 August to 10 December 2024 yielded the site’s first continuous daytime $r_0$ dataset, with values ranging from 1.5 cm to 12 cm and a mean of 4.09 cm. The compact SDIMM provides a cost-effective and easily deployable solution for comparative daytime $r_0$ assessments across multiple candidate astronomical sites. Full article

Heavy metal ions pose a significant threat to the environment and human health due to their high toxicity and bioaccumulation. Traditional instrumentations, although sensitive, are often complex, costly, and unsuitable for on-site rapid detection of heavy metal ions. Lateral flow assay technology has emerged as a research hotspot due to its rapid, simple, and cost-effective advantages. This review summarizes the applications of lateral flow assay technology based on nucleic acid molecules and antigen–antibody interactions in heavy metal ion detection, focusing on recognition mechanisms such as DNA probes, nucleic acid enzymes, aptamers, and antigen–antibody binding, as well as signal amplification strategies on lateral flow testing strips. By incorporating these advanced technologies, the sensitivity and specificity of lateral flow assays have been significantly improved, enabling highly sensitive detection of various heavy metal ions, including Hg²⁺, Cd²⁺, Pb²⁺, and Cr³⁺. In the future, the development of lateral flow assay technology for detection of heavy metal ions will focus on multiplex detection, optimization of signal amplification strategies, integration with portable devices, and standardization and commercialization. With continuous technological advancements, lateral flow assay technology will play an increasingly important role in environmental monitoring, food safety, and public health. Full article

This review explores optical technologies for non-invasive glucose monitoring (NIGM) using aqueous humor (AH) as media, addressing the limitations of traditional invasive methods in diabetes management. It analyzes key techniques such as Raman spectroscopy, polarimetry, and mid- and near-infrared spectral methods, highlighting their respective challenges, alongside emerging hybrid approaches like photoacoustic spectroscopy and optical coherence tomography. Crucially, the practical realization of these optical methods for portable NIGM hinges on advanced instrumentation. Therefore, this review also details progress in compact NIR spectrometers. While conventional systems often lack suitability, significant advancements in on-chip technologies—including miniaturized dispersive spectrometers and various on-chip Fourier transform systems (e.g., spatial heterodyne, stationary wave integral, and temporally modulated FT systems)—utilizing integration platforms like SOI and SiN are promising. Such innovations offer the potential for high spectral resolution, large bandwidth, and miniaturization, which are essential for developing practical AH-based NIGM systems to improve diabetes care. Full article

Alzheimer’s disease (AD) early screening requires non-invasive, high-sensitivity detection of low-abundance biomarkers in complex biofluids like saliva. In this study, we present a miniaturized, silicon-based electrochemical sensor for sequential detection of two AD salivary biomarkers, lactoferrin (Lf) and amyloid β-protein 1-42 (Aβ_1-42), on a single reusable electrode. The sensor features a three-electrode system fabricated by sputter-coating a quartz substrate with gold (Au) sensing electrodes, which are further modified with gold nanoparticles (AuNPs) to form 3D dendritic structures that enhance surface area and electron transfer. To improve specificity, immunomagnetic beads (MBs) are employed to selectively capture and isolate target biomarkers from saliva samples. These MB–biomarker complexes are introduced into a polydimethylsiloxane chamber aligned with Au sensing electrodes, where a detachable magnet localizes the complexes onto the electrode surface to amplify redox signals. The AuNPs/MBs sensor achieves detection limits of 2 μg/mL for Lf and 0.1 pg/mL for Aβ_1-42, outperforming commercial ELISA kits (37.5 pg/mL for Aβ_1-42) and covering physiological salivary concentrations. After the MBs capture the biomarkers, the sensor can output the result within one minute. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) measurements confirm enhanced electron transfer kinetics on AuNP-decorated surfaces, while linear correlations (R² > 0.95) validate quantitative accuracy across biomarker ranges. The compact and integrated design eliminates reliance on bulky instrumentation and enables user-friendly operation, establishing a promising platform for portable, cost-effective AD screening and monitoring. Full article

A simple and rapid smartphone-based colorimetric assay for peracetic acid concentration was developed to facilitate point-of-use disinfectant preparations for infection prevention and control. The colorimetric detection was based on the oxidation of N,N-diethyl-phenylenediamine by peracetic acid through an intermediate reaction with potassium iodide, resulting in pink-magenta products. The colorimetric reaction was performed on a 96-well plate; then, the color products were photographed in one image. The color intensity was evaluated to determine the peracetic acid concentration using a custom-built mobile application named Modern Peracetic Acid Analysis. The relative green intensity of the pink-magenta products was directly proportional to the peracetic acid concentration in the range of 0.15 to 3.0 µg/mL. The detection and quantitation limits were 0.11 µg/mL and 0.34 µg/mL, respectively. The approach was successfully applied to determine the peracetic acid concentration in pharmaceutical disinfectant formulations. The results obtained using the proposed approach showed no significant differences from those obtained using acid–base titration at the 95% confidence level. The greenness of the proposed approach was evaluated using the Complementary Green Analytical Procedure Index, Analytical Greenness, and Blue Applicability Grade Index, demonstrating enhanced environmental friendliness and practical advantages, as well as simple, portable instrumentation that is easier to operate than traditional spectrophotometric and titration methods. Furthermore, a sustainability assessment based on the Need, Quality, and Sustainability index underscored its enhanced sustainability. Full article

The mineral composition of table salt can be indicative of its origin. This work evaluated the possibility of identifying the origin of salt from four countries: Brazil, Spain, France, and Portugal. Eight metals were quantified through flame atomic absorption/emission spectroscopy (FAAS). The possibility of using portable near-infrared spectroscopy (NIR) as a faster and lower-cost alternative for identifying salt provenance was also evaluated. The content of Ca, Mg, Fe, Mn, and Cu was identified as possible markers to differentiate the salt origin. One-class classifiers using FAAS data and DD-SIMCA could discriminate the salt origin with few misclassifications. For NIR spectroscopy, it was possible to highlight the importance of controlling the humidity and granulometry before the spectra acquisition. After drying and milling the samples, it was possible to discriminate between samples based on the interaction between the water of hydration and the presence of the cations in the sample. The Mg, Mn, and Cu are important in identifying the origin of salt using NIR spectra. The DD-SIMCA model using NIR spectra could classify the origin with the same performance as observed in FAAS. However, it is important to emphasize that NIR spectroscopy requires less sample preparation, is faster, and has low-cost instrumentation. Full article

The eddy current testing (ECT) technique enables efficient and non-destructive conductivity measurement. However, conventional ECT is significantly influenced by the thickness of the material, often resulting in the arbitrary selection of excitation frequency. In addition, complex inverse calculations in the eddy current analytical model pose challenges for practical application. This paper proposes a method for measuring the conductivity of non-ferromagnetic materials based on a simplified analytical model. Firstly, the classical Dodd–Deeds analytical model is simplified based on the electromagnetic properties of materials under high-frequency conditions, resulting in a simplified model that directly relates the coil impedance phase to the material’s conductivity. Furthermore, in combination with a finite element method (FEM) analysis, a frequency selection criterion is proposed, and a corresponding measurement method is developed. This method enables direct conductivity calculation by substituting the measured coil impedance phase into the simplified model. Finally, experiments were conducted to verify the effectiveness of the proposed method. The results demonstrate that the proposed method accurately measures the conductivity of non-ferromagnetic materials over a range of 0.5–58.5 MS/m, achieving absolute and relative errors less than 1.05 MS/m and 1.83%, respectively, without requiring complex inversion calculations or multiple calibrations. This advancement in measurement principles provides a new theoretical foundation and technical pathway for developing online inspection systems and portable instrumentation. Full article

This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major soybean components and for the development of portable near-infrared (NIR) instruments. Thirty soybean samples from diverse sources were collected, and 360 spectral measurements were acquired using a 900–1700 nm NIR spectrometer after grinding and standardized sampling. To improve model robustness, preprocessing strategies such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay derivatives were applied. Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). Comparative analysis revealed that the RF model consistently outperformed the others across most combinations. Specifically, the SPASNV + D₁–RF combination achieved an RPD of 14.7 for moisture, CARS–SNV + D₁–RF reached 5.9 for protein, and CARS–SG + D₂–RF attained 12.0 for fat, all significantly surpassing alternative methods and demonstrating a strong nonlinear learning capacity and predictive precision. These findings show that integrating optimal preprocessing and feature selection strategies can markedly enhance the predictive accuracy in NIR-based soybean analyses. The RF model offers exceptional stability and performance, providing both technical reference and theoretical support for the development of portable NIR devices and practical rapid-quality assessment systems for soybeans in industrial applications. Full article

Leaf chlorophyll content is an important indicator of the health status of pear trees. This study used Korla fragrant pears, a Xinjiang regional product, to investigate methods for estimating the relative chlorophyll content of pear leaves. Samples were collected from pear trees in the east, south, west, and north positions of peripheral canopy leaves. The leaf soil plant analysis development (SPAD) method was implemented using a SPAD-502 laser chlorophyll meter. The instrument measures the relative chlorophyll content as the SPAD value. Leaf spectra were acquired using a portable field spectrometer, ASD FieldSpec4. ViewSpecPro 6.2 software was employed to smooth the ground spectral data. Traditional mathematical transformations and the discrete wavelet transform were used to process the spectral data, then correlation analysis was employed to extract the sensitive bands, and partial least squares regression (PLS) was used to establish a model for estimating the chlorophyll content of pear tree leaves. The findings indicate that (1) the models developed using the discrete wavelet transform had coefficients of determination (R²) exceeding 0.65, and their predictive performance surpassed that of other models employing various mathematical transformations, and (2) the model constructed using the L1 scale for the discrete wavelet transform had greater estimation accuracy and stability than models established through traditional mathematical transformations or the high-frequency scale for discrete wavelet transform, with an R² value of 0.742 and a root mean square error (RMSE) of 0.936. The prediction model for relative chlorophyll content established in this study was more accurate for chlorophyll monitoring in pear trees, and thus, it provided a new method for rapid estimation. Moreover, the model provides an important theoretical basis for the efficient management of pear trees. Full article

The pinewood nematode (PWN), Bursaphelenchus xylophilus, is a pathogenic organism that causes pine wilt disease (PWD). To date, several molecular diagnostic methods have been developed; however, rapid, convenient, and inexpensive field diagnostic tools for detecting PWN are still limited. In this study, an enzyme-mediated duplex exponential amplification (EmDEA) method for detecting PWN from extracted nematodes or pinewood sawdust was developed and tested. This method comprised an EmDEA molecular test kit, which consisted of freeze-dried enzyme pellets that can be stored at room temperature (approximately 20–25 °C) for one year, a dry block heater, and a portable isothermal fluorescence amplification instrument. The whole procedure was completed within 30 min. The EmDEA assay could detect a single PWN at all life stages from a mixture of other nematode species or from pinewood sawdust. The detection limit was 10 copies (plasmid weight 32.66 ag) or 1/500 of that of a single adult PWN per reaction. Therefore, the EmDEA assay has potential applications in PWN detection in the field, as well as quarantine inspection in international trade. Moreover, modification of primers and probes will allow the rapid identification of other nematode species. Full article

This work presents a comprehensive investigation of tropospheric NO₂ measurements using a portable ground-based Pandora spectrometer, incorporating an independently designed and implemented calibration and retrieval process (P-CAR v1.0). We designed and optimized a region-specific algorithm for retrieving tropospheric NO₂ column densities in China. The measurement process began with establishing a spectral calibration system for processing the Pandora’s raw observations, followed by enhancing the differential optical absorption spectroscopy (DOAS) algorithm to retrieve both the slant column densities (SCDs) and tropospheric vertical column densities (VCDs) of NO₂. To validate our retrieval products, comparative analyses were conducted against co-located MAX-DOAS measurements. The results demonstrate excellent agreement between Pandora-retrieved tropospheric NO₂ and MAX-DOAS observations, with correlation coefficients exceeding 0.96 for both hourly and daily mean VCDs and fitting slopes greater than 0.90. Furthermore, the validation extended to multi-satellite observations from the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI), exhibiting pronounced consistency, as evidenced by the correlation coefficients all surpassing 0.90 for the hourly mean values. These findings confirm the high accuracy and reliability of NO₂ retrievals from the portable Pandora instrument, significantly boosting its potential for atmospheric monitoring and application. Full article

Volatile organic compounds (VOCs) in urine headspace are potential biomarkers for different medical conditions, as canines can detect human diseases simply by smelling VOCs. Because dogs can detect disease-specific VOCs, gas chromatography–mass spectrometry (GC–MS) systems may be able to differentiate medical conditions with enhanced accuracy and precision, given they have unprecedented efficiency in separating, quantifying, and identifying VOCs in urine. Advancements in instrumentation have permitted the development of portable GC–MS systems that analyze VOCs at the point of care, but these are designed for environmental monitoring, emergency response, and manufacturing/processing. The purpose of this study is to repurpose the HAPSITE^® ER portable GC–MS for identifying urinary VOC biomarkers. Method development focused on optimizing sample preparation, off-column conditions, and instrumental parameters that may affect performance. Once standardized, the method was used to analyze a urine standard (n = 10) to characterize intra-day reproducibility. To characterize inter-day performance, n = 3 samples each from three volunteers (and the standard) were analyzed each day for a total of four days (n = 48 samples). Results showed the method could detect VOC signals with adequate reproducibility and distinguish VOC profiles from different volunteers with 100% accuracy. Full article

Ground reaction force (GRF) and center of pressure (COP) during walking are two important measures that could be used in a range of applications, from the control of devices such as exoskeletons to clinical assessments. Recording these measures requires fixed laboratory equipment such as force plates or expensive portable insoles. We present an alternative approach by developing a 3D-printed insole that uses pneumatic chambers and pressure sensors to estimate the net GRF and the anterior–posterior COP position. The intentionally simple design, using just two pneumatic chambers, can be fabricated using standard 3D printing technology and readily available soft materials. We used experimentally recorded data from a motion capture system along with parameter identification techniques to characterize and validate the insole while walking at different speeds. Our results showed that the insole was capable of withstanding repeated loading during walking—up to 1.2 times the body weight—and possessed a bandwidth high enough to capture gait dynamics. The identified models could estimate the GRF and the anterior–posterior COP position with less than 9% error. These results compare favourably with those of commercially available instrumented insoles and can be obtained at a fraction of their cost. This low-cost yet effective solution could assist in applications where it is important to record gait outside of laboratory conditions, but the cost of commercial solutions is prohibitive. Full article