Detection Applications Research Articles

Graphene-PbS Quantum Dot Heterostructure for Broadband Photodetector with Enhanced Sensitivity.

Photodetectors converting light into electrical signals are crucial in various applications. The pursuit of high-performance photodetectors with high sensitivity and broad spectral range simultaneously has always been challenging in conventional semiconductor materials. Graphene, with its zero bandgap and high electron mobility, is an attractive candidate, but its low light absorption coefficient restricts its practical application in light detection. Integrating graphene with light-absorbing materials like PbS quantum dots (QDs) can potentially enhance its photodetection capabilities. Here, this work presents a broadband photodetector with enhanced sensitivity based on a graphene-PbS QD heterostructure. The device leverages the high carrier mobility of graphene and the strong light absorption of PbS QDs, achieving a wide detection range from ultraviolet to near-infrared. Employing a simple spinning method, the heterostructure demonstrates ultrahigh responsivity up to the order of 107 A/W and a specific detectivity on the order of 1013 Jones, showcasing significant potential for photoelectric applications.

Intercalation-enhanced topological material Bi2Se3: Tuning charge carrier and phonon dynamics for advanced optoelectronic and terahertz applications

The field of topological materials (TMs) is experiencing significant advancements due to their unique surface states, which offer considerable potential for optoelectronics and terahertz (THz) applications. Intercalation in TMs can effectively modulate these surface states, thereby altering electronic and optical properties and enhancing functionalities. This approach enables the tailoring of material properties for specific applications, broadening the utility of TMs across various technological domains. This study focuses on understanding the charge carrier and phonon dynamics in bismuth selenide (Bi2Se3) intercalated with various metals (AxBi2Se3, where A = Ag, Cr, Ni, Mn, Sm, Zn). We examine carrier trapping, carrier relaxation through optical and acoustic phonons, charge recombination processes, and variations in carrier temperature with probe delay lifetimes. Additionally, we explore coherent optical phonons (COPs) in MIBS, which oscillate at THz frequencies and have potential applications in THz generation and detection. Achieving tunability in the THz frequency of COP modes is a significant challenge; however, our research establishes a correlation between optical and structural properties and the dependence of COP frequencies on effective metal intercalation. This comprehensive investigation elucidates the intricate interplay between surface carriers and phonon dynamics in intercalated TMs, highlighting promising advancements for diverse technological applications, including spintronics, optoelectronics, and THz technology.

Development of PANI-CNT electrochemical sensor with NiO catalysis application in methanol detection

Development of PANI-CNT electrochemical sensor with NiO catalysis application in methanol detection

Application of a dual-modality colorimetric analysis methodto inkjet printing lateral flow detection of Salmonella typhimurium.

Lateral flow assay (LFA) color signal quantification methods were developed by utilizing both International Commission on Illumination (CIE) LAB (CIELAB) color space and grayscale intensity differences. The CIELAB image processing procedure included calibration, test, control band detection, and color difference calculation, which can minimize the noise from the background. The LFA platform showcases its ability to accurately discern relevant colorimetric signals. The rising occurrence of infectious outbreaks from foodborne pathogens like Salmonella typhimurium presents significant economic, healthcare, and public health risks. The study introduces an aptamer-based lateral flow (ABLF) platform by using inkjet printing for specially detecting S. typhimurium. The ABLF utilized gold-decorated polystyrene microparticles, functionalized with specific S. typhimurium aptamers (Ps-AuNPs-ssDNA). The platform demonstrates a detection limit of 102 CFU mL-1 in buffer solutions and 103 CFU mL-1 in romaine lettuce tests. Furthermore, it sustained performance for over 8 weeks at room temperature. The ABLF platform and analysis methods are expected to effectively resolve the low-sensitivity problems of the former LFA systems and to bridge the gap between lab-scale platforms to market-ready solutions by offering a simple, cost-effective, and consistent approach to detecting foodborne pathogens in real samples.

Synthesis of dragonfruit-based silicon nanoparticles at room temperature and their application in the real-time fiber-optic detection of doxorubicin

Doxorubicin (DOX) has been extensively used in cancer chemotherapy as an antibiotic drug, but when DOX concentration exceeds the threshold value, irreversible liver toxicity and cardiotoxicity can be triggered. Therefore, it is a significant theme to detect DOX concentration of patient blood and metabolites for the optimization of drug dosage. Linear correlation between fluorescence quenching of silicon nanoparticles (SiNPs) and DOX concentration enables SiNPs to present good prospect to address this tricky issue. Inspired by these aspects, we put forward a novel method to synthesize dragonfruit-based SiNPs (DSiNPs) at room temperature, and leverage them in the fiber-optic sensing of urine DOX. Firstly, we investigate a simple and green synthesis method of DSiNPs emitting blue-green fluorescence through a one-step redox reaction at room temperature with organosilane as silicon source and fresh dragonfruit juice as reducing agent. Secondly, depending on optical characterization, we demonstrate mechanism of DSiNPs in the detection of DOX. Thirdly, a fiber-optic sensor is used to measure the fluorescence intensity of DSiNPs under diverse salt concentrations, pH values, and illumination time to explore the tolerance of DSiNPs. Fourthly, based on optimization of parameters and validation of specificity, we acquire linear response range of this method to DOX from 0 to 50 μM with detection limit of 3 μM. Finally, this method is leveraged in the detection of urine DOX, and obtained the recovery rates of 92.30 % and 107.30 %. This method holds excellent potential in the clinical analysis of anti-tumor drugs for the advantages of environmental friendliness and simple operation.

Star-shaped flexible arm multivalent aptamers for largely improved virus binding affinity

Here we explore the click chemistry-based synthesis of high affinity multivalent aptamers and their application for virus detection. In contrast to attempts based on rigid constructs, such as aptamer-modified nanoparticles or hybridization-connected constructs, we propose highly flexible “star-shaped” multivalent aptamers to facilitate the cooperative binding to randomly distributed viral proteins on the surface of pleomorphic viruses such as the respiratory syncytial virus (RSV). Polyethylene glycols (PEGs) with 4 azide terminated flexible arms spanning from a pentaerythritol core were conjugated to aptamer strands with dibenzocyclooctyne and a fluorescent label at their, 3’ and 5’ terminus, respectively. By controlling the aptamer - PEG ratio and following electrophoretic separation all the different valency aptamer conjugates could be prepared. For proof of concept, we used an aptamer selected for the G protein of RSV. We show that significantly higher apparent affinities can be obtained by using multivalent aptamers for both the G protein and RSV virus than with the single stranded aptamer, i.e. with up to ca. 2 orders of magnitude. The inherent convenience of synthesizing fluorescently labelled multivalent aptamers leads to a versatile class of affinity ligands for sensing as demonstrated through the improved detection sensitivity of tetravalent aptamer tagged RSV by fluorescence nanoparticle tracking analysis.

Synthesis of Mo-Pt/CeO2 Dual Single-Atom Nanozyme for Multifunctional Biochemical Detection Applications.

Elaborated structural modulation of Pt-based artificial nanozymes can efficiently improve their catalytic activity and expand their applications in clinical diagnosis and biochemical sensing. Herein, a highly efficient dual-site peroxidase mimic composed of highly dispersed Pt and Mo atoms is reported. The obtained Mo-Pt/CeO2 exhibits exceptional peroxidase-like catalytic activity, with a Vmax as high as 34.16 × 10-8 m s-1, which is 37.5 times higher than that of the single-site counterpart. Mechanism studies suggest that the Mo atoms can not only serve as adsorption and activation sites for the H2O2 substrate but also regulate the charge density of Pt centers to promote the generation ability of •OH. As a result, the synergistic effect between the dual active sites significantly improves the catalytic efficiency. Significantly, the application of the Mo-Pt/CeO2 catalyst's excellent peroxidase-like activity is extended to various biochemical detection applications, including the trace detection of glucose and cysteine, as well as the assessment of antioxidants' antioxidant capacity. This work reveals the great potential of rational design dual-site active centers for constructing high-performance artificial nanozymes.

Data for AI in Congenital Heart Defects: Systematic Review.

Congenital heart disease (CHD) represents a significant challenge in prenatal care due to low prenatal detection rates. Artificial Intelligence (AI) offers promising avenues for precise CHD prediction. In this study we conducted a systematic review according to the PRISMA guidelines, investigating the landscape of AI applications in prenatal CHD detection. Through searches on PubMed, Embase, and Web of Science, 621 articles were screened, yielding 28 relevant studies for analysis. Deep Learning (DL) emerged as the predominant AI approach. Data types were limited to ultrasound and MRI sequences mainly. This comprehensive analysis provides valuable insights for future research and clinical practice in CHD detection using AI applications.

Digital Cognitive Test for Early Detection of Dementia.

Dementia poses a significant global health problem, necessitating early detection for effective intervention. A literary review was conducted to examine current mobile-based cognitive tests. The limitations of the reviewed applications varied from accessibility to reliability. The paper addresses these limitations by proposing a digital, self-administered version of the Montreal Cognitive Assessment (MoCA) test, with future research focusing on validating the effectiveness of the proposed mobile application in early detection.

Magnetic Flux Concentration Technology Based on Soft Magnets and Superconductors

High-sensitivity magnetic sensors are fundamental components in fields such as biomedicine and non-destructive testing. Flux concentration technology enhances the sensitivity of magnetic sensors by amplifying the magnetic field to be measured, making it the most effective method to improve the magnetic field resolution of magnetic sensors. Superconductors and high-permeability soft magnetic materials exhibit completely different magnetic effects. The former possesses complete diamagnetism, while the latter has extremely high magnetic permeability. Both types of materials can be used to fabricate flux concentrators. This paper compares superconducting and soft magnetic flux concentration technologies through theoretical simulations and experiments, investigating the impact of different structural parameters on the magnetic field amplification performance of superconducting and soft magnetic concentrators. This research is significant for the development of magnetic focusing technology and its applications in weak magnetic detection and other fields.

Real-time chlorophyll-a forecasting using machine learning framework with dimension reduction and hyperspectral data

Since water is an essential resource in various fields, it requires constant monitoring. Chlorophyll-a concentration is a crucial indicator of water quality and can be used to monitor water quality. In this study, we developed methods to forecast chlorophyll-a concentrations in real-time using hyperspectral data on IoT platform and various machine learning algorithms. Compared to regular cameras that record information only in the three broad color bands of red, green, and blue, the hyperspectral images of drinking water sources record the data in dozens or even hundreds of distinct small wavelength bands, providing each pixel in an image with a full spectrum. Different machine learning algorithms have been developed using hyperspectral data and field observations of water quality and weather conditions. Previous studies have predicted chlorophyll concentrations using either partial least squares (PLS), which is a dimensionality reduction method, or machine learning. In contrast, our study employed the PLS technique as a preprocessing step to diminish the dimensionality of the hyperspectral data, followed by the application of the machine learning techniques with optimized hyperparameters to improve the precision of the predictions, thereby introducing a real-time mechanism for chlorophyll-a prediction. Consequently, a machine learning algorithm with R2 values of 0.9 or above and sufficiently small RMSE was developed for real-time chlorophyll-a forecasting. Real-time chlorophyll-a forecasting using LightGBM has the best performance, with a mean R2 of 0.963 and a mean RMSE of 2.679. This paper is expected to have applications in algal bloom early detection on monitoring systems.

Thermal decomposition-assisted, aspect ratio controlled ZnO nanorods towards highly selective H2 gas detection

ZnO nanostructures with various aspect ratios have been synthesized for H2 gas detection applications. The thermal-decomposition method was employed at different annealing temperatures (350, 450, and 550 °C) and its impact on various shapes/sizes of ZnO nanostructures is demonstrated. Thermal decomposition performed at 350 °C exhibited a maximum (∼6.25) aspect ratio among them. Its capability of H2 sensing was also observed to be maximum by realizing ∼483% of sensor response at 180 °C under H2 gas concentration of 80 ppm. The sensor response is ∼3 times (∼177%) and ∼9 times (∼53%) higher at ZnO nanostructure synthesized at 350 °C than at 450 °C, and 550 °C, respectively. The higher sensor response has been attributed to the increased availability of active surface area for adsorption/desorption of gas molecules. ZnO@350 nanostructure showed significantly higher selectivity towards H2 gas than other target chemical inputs. We have also studied H2-induced metallization on the surface of ZnO nanostructures which plays an important role for improving the selectivity and sensor response. This study provides insight into the role of aspect-ratio-controlled shape/sized ZnO in improving H2 gas sensing.

Semi-supervised learning for real-time anomaly detection in pulsed transfer wire arc additive manufacturing

In Wire-Arc Additive Manufacturing (WAAM), ensuring the quality and integrity of components is of key importance and performing anomaly detection during production is an efficient strategy for preventing defects and maintaining an acceptable quality with lower costs compared to the development of complex supervised learning techniques. This paper presents an approach based on semi-supervised learning for real-time anomaly detection in the production of Inconel 718 components via the Pulsed Transfer WAAM process. The workflow is based on the use of training data obtained by employing established process parameters, similar to what is done in Welding Procedure Qualification Records (WPQRs), to develop a semi-supervised anomaly detection application. The proposed approach involves depositing material using the already established process parameters and collecting the corresponding welding data in terms of welding current and voltage sensor signals. The collected data are then employed to train an unsupervised algorithm by using solely good deposition data to learn hidden complex patterns of normality and hence detect anomalies based on deviations from these patterns. With this aim, global and local features are extracted from the welding current and voltage sensor signals in both time and time-frequency domains via Wavelet Transform technique and a deep learning approach based on a residual convolutional autoencoder. To showcase the effectiveness of the proposed approach, a case study involving wire arc additive manufacturing of Inconel 718 components was conducted. The proposed algorithm was tested and achieved an F-score of 0.895, representing a 30 % improvement over state-of-the-art methods that rely on time domain features for anomaly detection. This research work contributes to the improvement of anomaly detection methodologies, offering substantial advantages over alternative techniques for quality control and reliability of additive manufacturing processes such as WAAM.

Cesium lead bromide perovskite nanocrystals as fluorescent taggants for detecting fluid leaks

Fluorescence tagging represents a low cost, fast, and reliable method for leak detection in the chemical industry. Here, the use of halide perovskites as fluorescent taggants is demonstrated to detect piping leakages. To this end, cesium lead bromide (CsPbBr3) nanocrystals were synthesized at room temperature and dissolved in silicon oil as a green luminophore (530 ± 5 nm). The resulting system had a photoluminescence quantum yield of 48 ± 2 % while retaining key optical properties (emission linewidth and lifetime) over the timescale required for typical leak detection (2–30 minutes), even under strongly acidic/basic conditions (pH 1–13) and high pressures (up to 7 atm). Crucially, the operating dose of CsPbBr3 required for leak detection is an order of magnitude lower than those used for typical commercial fluorescent taggants (0.03 versus 0.003 oz mL−1). These results strongly suggest the great potential of halide perovskites towards industrial leak detection applications.

UV-ozone treated glass fiber based lateral flow DNA extraction platform integrated with LAMP for rapid detection of pathogen bacteria in whole blood

The need for a fast and reliable platform for the detection of pathogens in point-of-care (POC) applications is increasing day by day. Molecular detection of pathogens is made possible by a good DNA isolation method, which allows for efficient and pure extraction from the cell. To address this need, our work presents a new lateral flow platform based on UV ozone-treated glass fiber pads for high-throughput DNA isolation from very low volumes of bacterial samples. The platform we developed mainly consists of UV ozone-treated glass fiber, which serves as the essential component binding DNA, and a cellulose-based absorbent pad acting as a reservoir for washing buffers and undesired substances from bacterial cells. UV ozone treatment increased the DNA recovery efficiency of glass fiber by 2.5 times compared to untreated glass fiber. qPCR results confirmed the isolation of pure DNA from both gram-negative and gram-positive bacteria samples performed on the developed platform. For five different bacteria concentrations (10–109 cfu/mL), a good linearity between Cq values and bacteria concentrations was obtained. A minimum concentration of 10 cfu/mL could be detected for both bacterial samples of 10 µL. The developed DNA extraction platform was also integrated with a portable heater for the LAMP-based detection application. We successfully demonstrated that the proposed extraction platform, combined with the colorimetric LAMP-based system, can detect Staphylococcus aureus bacteria spiked in whole blood samples in under one hour, including both extraction and detection processes. Our simplified paper-based platform will be a good alternative for the DNA extraction and molecular detection of pathogens without the need for complex laboratory equipment in POC applications.

Preparation of laser-induced graphene modified electrode for the detection of bisphenol A in plastics

In this paper, laser-induced graphene (LIG) with high porosity and large specific surface area was prepared by laser induction technology. Then a selective electrochemical sensor based on LIG was proposed for determination of bisphenol A (BPA). Under optimal conditions, the developed sensor emerged an efficient electrocatalytic activity towards the redox of BPA. The results show that it presents a wide detection range of 100 nM ∼ 1.0 mM and a low detection limit of 50.68 nM with good anti-interference and stability. Furthermore, the sensor can be applied to detect BPA in real plastic samples with the recovery rate of 98.6∼105.8 %, which indicated that the sensor has potential for future applications in trace detection of BPA.

Comprehensive Evaluation of Machine Learning and ANN Models for Breast Cancer Detection

Breast cancer is one of the most prevalent forms of cancer among women, and early diagnosis is of vital importance. In recent years, machine learning algorithms have demonstrated high accuracy in breast cancer detection, contributing to earlier diagnoses. Various machine learning models can analyze tumor characteristics and assist in cancer identification and treatment decisions. This project aims to comprehensively examine the performance of 14 different machine learning algorithms and a custom-developed Artificial Neural Network (ANN) model in breast cancer detection, using public Wisconsin dataset. Following data preprocessing, the training and testing stages are carried out, and the results are thoroughly analyzed to determine the model with the highest accuracy. The findings of this project will showcase the potential impact of machine learning algorithms in the clinical applications of breast cancer detection, as researchers and healthcare providers can leverage these advanced computational techniques to enhance the accuracy and efficiency of breast cancer diagnosis, ultimately leading to improved patient outcomes and the advancement of this critical field of oncology.

Synergic Surface Modifications of PbS Quantum Dots by Sodium Acetate in Solid-State Ligand Exchange toward Short-Wave Infrared Photodetectors.

PbS quantum dots (QDs) are promising for short-wave infrared (SWIR) photodetection and imaging. Solid-state ligand exchange (SSLE) is a low-fabrication-threshold QD solid fabrication method. However, QD treatment by SSLE remains challenging in seeking refined surface passivation to achieve the desired device performance. This work investigates using NaAc in the ligand exchange process to enhance the film morphology and electronic coupling configuration of QD solids. By implementing various film and photodetector device characterization studies, we confirm that adding NaAc with a prominent adding ratio of 20 wt % NaAc with tetrabutylammonium iodide (TBAI) in the SSLE leads to an improved film morphology, reduced surface roughness, and decreased trap states in the QD solid films. Moreover, compared to the devices without NaAc treatment, those fabricated with NaAc-treated QD solids exhibit an enhanced performance, including lower dark current density (<100 nA/cm2), faster response speed, higher responsivity, detectivity, and external quantum efficiency (EQE reaching 25%). The discoveries can be insightful in developing efficient, low-cost, and low-fabrication-threshold QD SWIR detection and imager applications.

Detection dataset of electric bicycles for lift control

Electric bicycle datasets for lift control have played a crucial role in the development of electric bicycle lift entry detection algorithms. However, existing datasets often encounter issues, including a small dataset size, a high false detection rate, and the absence of a publicly available benchmark dataset. This paper addresses these challenges by constructing a new electric bicycle detection dataset for lift control based on the research demands of the electric bicycle detection task for lift control. XHNet_EB, a car scene image dataset covering various types of electric bicycles, was constructed based on images taken by a camera from above. Segmented annotation was employed, framing the front and rear of the electric bicycle independently. This approach effectively addressed the problems of many irrelevant features and a high false detection rate caused by the use of whole-electric-bicycle annotations in mainstream datasets. AP_50, AP_75, the mAP, and the number of false detections in the images were used for quantitative analysis of the dataset. The experimental results indicated that the object detection accuracy of the XHNet_EB dataset was excellent. Quantitative analysis and evaluation of the number of false detections in images were conducted using four mainstream lightweight detection model algorithms. The results demonstrated that segmented annotation reduced the false detection rate more effectively than entire electric bicycle annotation. This study identified drawbacks in existing datasets. The dataset proposed in this paper overcomes the shortcomings of existing commercial data and solves problems such as the high false detection rate caused by the inclusion of many irrelevant features caused by the “whole-electric-bicycle annotation” method, which could help with the development of electric bicycle detection applications in lifts.

Quantum-enhanced metrology with large Fock states

AbstractQuantum metrology uses non-classical states, such as Fock states with a specific number of photons, to achieve an advantage over classical sensing methods. Typically, quantum metrological performance can be enhanced by increasing the involved excitation numbers, for example, by using large-photon-number Fock states. However, manipulating these states and demonstrating a quantum metrological advantage is experimentally challenging. Here we present an efficient method for generating large Fock states approaching 100 photons within a superconducting microwave cavity through the development of a programmable photon number filter. Using these states in displacement and phase measurements, we demonstrate quantum-enhanced metrology approaching the Heisenberg scaling for 40-photon Fock states and achieve a maximum metrological gain of up to 14.8 dB, highlighting the metrological advantages of large Fock states. Our study could be readily extended to mechanical and optical systems, promising potential applications in weak force detection and dark matter searches.