Sensor System For Detection Research Articles

Magnetoelastic Immunosensor for the Rapid Detection of SARS-CoV-2 in Bioaerosols.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a protein-coated, single-stranded RNA virus that parasitizes and infects primates, including humans. The current detection methods are mainly carried out using humans as a medium, such as a chest computed tomography (CT) examination, nucleic acid detection, antibody detection, and antigen detection. In addition, SARS-CoV-2 in bioaerosols is an important way of transmission and infection, which has attracted wide attention. In this paper, we simulate a sampling system of SARS-CoV-2 in bioaerosols and detect SARS-CoV-2 in bioaerosols by means of a magnetoelastic (ME) sensing device based on an Android intelligent terminal, which can directly detect SARS-CoV-2 in bioaerosols in a certain unit environment. This approach aims to achieve early detection and prevention. The experimental results show that the sampling system can successfully collect SARS-CoV-2 in bioaerosols. In the linear range of 5-20 ng/mL, the ME sensing detection system results closely match those of the calibration instrument (VNA), with goodness-of-fit values of 0.997 and 0.995, respectively. This work demonstrates that the ME sensing detection system proposed is stable, highly specific, real-time, rapid, and has certain reference values and feasibility.

Microsphere integrated convective amplification sensor for all-in-one-pot detection of Mycoplasma pneumoniae

The integration of isothermal amplification with CRISPR-Cas systems play a crucial role in the point-of-care test of infectious pathogens, owing to their specificity, sensitivity, and portability. However, these systems lack rigorous internal amplification control for monitoring false negatives and rely on carefully designed guide sequences, which barriers to their further promotion and application. Additionally, the complexity of sample purification and the laborious preparation of liquid reagents significantly limit these systems to ready-to-use and direct sample-to-answer detection in on-site diagnosis. Here, we combined ready-to-use lyophilized microsphere, thermal lysis of pathogens, guide-sequence-independent convective amplification, internal amplification control for monitoring negative false and a portable multi-channel instrument to develop a microsphere integrated convective amplification sensor (MICASOR) system for on-site detection. MICASOR integrated the overall workflow of DNA testing into one-pot to achieve all-in-one-pot detection for on-site diagnosis. MICASOR is a rapid, potable, ready-to-use and guide-sequence-independent platform and can monitor false negatives. MICASOR can detect as low as 1 copy/μL of clinical samples of Mycoplasma pneumoniae within 15 minutes. Validated by 30 real clinical samples, MICASOR showed 100 % agreement with gold standard real-time PCR. These results suggest that MICASOR has great potential to promote the control of infectious diseases in the outpatients, communities and poor resource areas.

An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring.

Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people's exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.

A Multi-Channel Urine Sensing Detection System Based on Creatinine, Uric Acid, and pH.

Urine analysis represents a crucial diagnostic technique employed in clinical laboratories. Creatinine and uric acid in urine are essential biomarkers in the human body and are widely utilized in clinical analysis. Research has demonstrated a correlation between the normal physiological concentrations of creatinine and uric acid in urine and an increased risk of hypertension, cardiovascular diseases, and kidney disease. Furthermore, the pH of urine indicates the body's metabolic processes and homeostatic balance. In this study, an integrated multi-channel electrochemical sensing system was developed, combining electrochemical analysis techniques, microelectronic design, and nanomaterials. The architecture of an intelligent medical detection system and the production of an interactive interface for smartphones were accomplished. Initially, multi-channel selective electrodes were designed for creatinine, uric acid, and pH detection. The detection range was 10 nM to 100 μM for creatinine, 100 μM to 500 μM for uric acid, and 4 to 9 for pH. Furthermore, interference experiments were also conducted to verify the specificity of the sensors. Subsequently, multi-channel double-sided sensing electrodes and function-integrated hardware were designed, with the standard equations of target analytes stored in the system's read-only memory. Moreover, a WeChat mini-program platform was developed for smartphone interaction, enabling off-body detection and real-time display of target analytes through smartphones. Finally, the aforementioned electrochemical detection electrodes were integrated with the smart sensing system and wirelessly interfaced with smartphones, allowing for intelligent real-time detection in primary healthcare and individual household settings.

Fluorescent aptasensor based on competitive recognition strategies and point-of-care testing system for brain natriuretic peptide detection

Brain natriuretic peptide (BNP) is the preferred biomarker for clinical analysis, widely used in early screening and prognostic monitoring of cardiovascular and neurological diseases. Due to the low concentration and short half-life of BNP in the blood, its sensitive detection remains a challenge that must be overcome. With the rapid development of molecular diagnosis and point-of-care testing (POCT), developing a BNP biosensor with high sensitivity, good stability, accuracy, speed, and low-cost is of great significance for clinical applications and emergency diagnosis. However, BNP in human blood exists in various forms, and traditional fluorescence sensors may lead to overestimation of the concentration. In this work, we constructed an “on” fluorescent aptasensor based on competitive recognition strategy for quantitative detection of BNP, and built a fluorescence sensing detection system based on smartphone to achieve rapid on-site detection. We designed a specific aptamer labeled with carboxyfluorescein (FAM) with a simple structure and high biological affinity for selective capture of BNP, and utilized the large specific surface area and excellent fluorescence quenching effect of carboxylated graphene oxide (GO-COOH) as a carrier and quencher for the aptasensor. By optimizing experimental parameters such as aptamer and GO-COOH concentrations, as well as quenching/recovery time, linear sensitive detection of BNP was achieved in the concentration range of 0.01 ng/mL–10 ng/mL, with a detection limit as low as 10 pg/mL, good specificity, and excellent application potential in artificial serum spiking experiments. Additionally, a stable and sensitive fluorescence signal collection system has been developed to meet the needs of portable detection, breaking through the usage environment of traditional fluorescence detection equipment and filling the gap of portable fluorescence biosensing. In summary, we have creatively proposed a fluorescent aptasensor that enables rapid and sensitive detection of BNP and the development of low-cost fluorescent biosensors. The combination with POCT has shown broad application prospects and also provides some ideas for the fluorescence detection of other protein biomarkers.

Research on the influence of different pyramid array structures on plane blackbody emissivity

The plane blackbody radiation source is pivotal to the infrared remote sensing detection system, with emissivity serving as a critical metric for its performance evaluation. This metric is predominantly influenced by the surface structure and coating properties. To improve emissivity, existing works have utilized pyramid structures to expand the contact area between the radiation surface and light, thereby augmenting light absorption. Nonetheless, there is relatively little research exploring the impact of pyramid structure on emissivity, leading to a scarcity of related emissivity data for plane blackbody. In this paper, we explored the impact on the blackbody emissivity induced by different pyramid structures, including the triangular pyramid array (TPA), the quadrangular pyramid array (QPA), the pentagonal pyramid array (PPA), and the hexagonal pyramid array (HPA). Also, the effects involving coating properties, the base-height ratio of the pyramid, and reflection characteristics on emissivity were investigated based on the Monte Carlo Method. Then, the specimens with diffuse reflection (DR) and near-specular reflection (NSR) were fabricated and tested experimentally. The uncertainties of simulation and experiment were maintained below 0.21 % and 0.50 %, respectively. Experimental results are well aligned with the corresponding simulation results, indicating that the normal emissivity of the TPA blackbody is 0.12 % higher than the QPA blackbody, and the directional emissivity uniform of the TPA blackbody is superior to that of the QPA blackbody for the DR model in the 8–14 μm waveband. Furthermore, the temperature field distributions of radiation surface for TPA and QPA blackbody were analysed, noting a maximum temperature difference of 50 mK and 30 mK, respectively. This paper provides experimental support and design references for further improving the emissivity of plane blackbodies.

Experimental implementation of skeleton tracking for collision avoidance in collaborative robotics

Collaborative robotic manipulators can stop in case of a collision, according to the ISO/TS 15066 and ISO 10218-1 standards. However, in a human-robot collaboration scenario where the robot and the human share the workspace, a better solution with concerns to production and operator safety would be to perceive in advance the presence of an obstacle and be able to avoid it, thereby completing the task without halting the robot. In this paper, an obstacle avoidance algorithm is tested using a sensor system for real-time human detection; the operator represents a potential dynamic obstacle that can interfere with the robot motion. The sensor system consists of three RGB-D cameras. A custom software framework has been developed in Python exploiting machine learning tools for human skeleton detection. The coordinates of the human body joints relative to the manipulator base are used as input to the obstacle avoidance algorithm. The use of multiple sensors makes it possible to limit the occlusion problem; in addition, the choice of non-wearable sensors goes in the direction of better operator comfort. A series of experimental tests were performed to verify the accuracy of skeleton detection and the ability of the system to avoid obstacles in real time. The human motion caption system, in particular, was validated through a comparison with a commercial system based on wearable IMU sensors widely used and validated in motion capture. There is an NRMSE of 3.23%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.23\\%$$\\end{document} for the RGB-D camera-based skeleton detection system, against an NRMSE of 12.23%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$12.23\\%$$\\end{document} for the IMU wearable sensor system. Test results confirm that the system is able to avoid collisions with the human body under various conditions, static or dynamic, ensuring a minimum safety distance to any part of the manipulator. In 44 tests in which the operator moves around the robot with possible collisions (at a speed typical of manufacturing operations), the minimum operator-robot distance averaged 208mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$208\\,\ extrm{mm}$$\\end{document}, being 200mm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$200\\,\ extrm{mm}$$\\end{document} the limit safety distance set by algorithm.

MANET based Integrated Sensor System for Disaster Detection and Communication in Hazardous Environments

MANET based Integrated Sensor System for Disaster Detection and Communication in Hazardous Environments

User Requirements and Perceptions of a Sensor System for Early Stress Detection in People With Dementia and People With Intellectual Disability: Qualitative Study.

Timely detection of stress in people with dementia and people with an intellectual disability (ID) may reduce the occurrence of challenging behavior. However, detecting stress is often challenging as many long-term care (LTC) residents with dementia and residents with ID have communication impairments, limiting their ability to express themselves. Wearables can help detect stress but are not always accepted by users and are uncomfortable to wear for longer periods. Integrating sensors into clothing may be a more acceptable approach for users in LTC. To develop a sensor system for early stress detection that is accepted by LTC residents with dementia and residents with ID, understanding their perceptions and requirements is essential. This study aimed to (1) identify user requirements for a garment-integrated sensor system (wearable) for early stress detection in people with dementia and people with ID, (2) explore the perceptions of the users toward the sensor system, and (3) investigate the implementation requirements in LTC settings. A qualitative design with 18 focus groups and 29 interviews was used. Focus groups and interviews were conducted per setting (dementia, ID) and target group (people with dementia, people with ID, family caregivers, health care professionals). The focus groups were conducted at 3 time points within a 6-month period, where each new focus group built on the findings of previous rounds. The data from each round were used to (further) develop the sensor system. A thematic analysis with an inductive approach was used to analyze the data. The study included 44 participants who expressed a positive attitude toward the idea of a garment-integrated sensor system but also identified some potential concerns. In addition to early stress detection, participants recognized other potential purposes or benefits of the sensor system, such as identifying triggers for challenging behavior, evaluating intervention effects, and diagnostic purposes. Participants emphasized the importance of meeting specific system requirements, such as washability and safety, and user requirements, such as customizability and usability, to increase user acceptance. Moreover, some participants were concerned the sensor system could contribute to the replacement of human contact by technology. Important factors for implementation included the cost of the sensor system, added value to resident and health care professionals, and education for all users. The idea of a garment-integrated sensor system for early stress detection in LTC for people with dementia and people with ID is perceived as positive and promising by stakeholders. To increase acceptability and implementation success, it is important to develop an easy-to-use, customizable wearable that has a clear and demonstrable added value for health care professionals and LTC residents. The next step involves pilot-testing the developed wearable with LTC residents with dementia and residents with ID in clinical practice.

An artificial intelligence-assisted microfluidic colorimetric wearable sensor system for monitoring of key tear biomarkers

The precise, simultaneous, and rapid detection of essential biomarkers in human tears is imperative for monitoring both ocular and systemic health. The utilization of a wearable colorimetric biochemical sensor exhibits potential in achieving swift and concurrent detection of pivotal biomarkers in tears. Nevertheless, challenges arise in the collection, interpretation, and sharing of data from the colorimetric sensor, thereby restricting the practical implementation of this technology. To overcome these challenges, this research introduces an artificial intelligence-assisted wearable microfluidic colorimetric sensor system (AI-WMCS) for rapid, non-invasive, and simultaneous detection of key biomarkers in human tears, including vitamin C, H+(pH), Ca2+, and proteins. The sensor consists of a flexible microfluidic epidermal patch that collects tears and facilitates the colorimetric reaction, and a deep-learning neural network-based cloud server data analysis system (CSDAS) embedded in a smartphone enabling color data acquisition, interpretation, auto-correction, and display. To enhance accuracy, a well-trained multichannel convolutional recurrent neural network (CNN-GRU) corrects errors in the interpreted concentration data caused by varying pH and color temperature in different measurements. The test set determination coefficients (R2) of 1D-CNN-GRU for predicting pH and 3D-CNN-GRU for predicting the other three biomarkers were as high as 0.998 and 0.994, respectively. This correction significantly improves the accuracy of the predicted concentration, enabling accurate, simultaneous, and quick detection of four critical tear biomarkers using only minute amounts of tears ( ~ 20 μL). This research demonstrates the powerful integration of a flexible microfluidic colorimetric biosensor and deep-learning algorithm, which holds immense potential to revolutionize the fields of health monitoring.

Smart Power Saving and Safety Management system Based on Smart Sensors Technologies

Comfort is becoming a major priority in the 21st century. So the revolutions of computing and smart environment came into existence. Some technologies like Ubiquitous/pervasive and ambient intelligence satisfy the maximum need of smart world but these technologies are not tightly coupled with the internet, so the people need another technology extension. Automation based on energy saving and safety is an ideal buzzing technology to influence new world technologies. In this project, an overview of smart automation systems is discussed. This project presents a design and prototype of the Automation system, With Motion sensor, LDR sensor , Fingerprint and smart theft detection system is introduced. In addition to that, LCD display would be used which will allow the user to monitor their devices smartly and gets information of security. Top of Form Keywords— Automation, Arduino Controller, LCD display, sensors , Load , Buzzer etc.

Multi-scale bioimpedance flexible sensing with causal hierarchical machine learning for fish vitality evaluation under adversity stress

It is expected that waterless low-temperature stressful environments will induce stress responses in fish and affect their vitality. In this study, we developed a laser-activated, stretchable, highly conductive liquid metal (LM) based flexible sensor system for fish multi-scale bioimpedance detection. It has excellent conformability, electrical conductivity, bending and cyclic tensile stability. Meanwhile, test result showed that wireless power supply is a potential solution for realizing safe power supply for devices inside waterless low-temperature packages. In addition, a hierarchical regression model (GC-HRM) based on Granger causality was established. The result showed that tissue bioimpedance can induce changes in individual bioimpedance with unidirectional Granger causality. The R2 of the linear regression (LR), support vector regression (SVR) and artificial neural network (ANN) models under single-scale individual bioimpedance were 0.85, 0.90 and 0.78, respectively. By adding the multi-scale bioimpedance features, the R2 of the LR, SVR and ANN models were improved to 0.95, 1.00 and 0.98, respectively.

Eating Event Recognition Using Accelerometer, Gyroscope, Piezoelectric, and Lung Volume Sensors.

In overcoming the worldwide problem of overweight and obesity, automatic dietary monitoring (ADM) is introduced as support in dieting practises. ADM aims to automatically, continuously, and objectively measure dimensions of food intake in a free-living environment. This could simplify the food registration process, thereby overcoming frequent memory, underestimation, and overestimation problems. In this study, an eating event detection sensor system was developed comprising a smartwatch worn on the wrist containing an accelerometer and gyroscope for eating gesture detection, a piezoelectric sensor worn on the jaw for chewing detection, and a respiratory inductance plethysmographic sensor consisting of two belts worn around the chest and abdomen for food swallowing detection. These sensors were combined to determine to what extent a combination of sensors focusing on different steps of the dietary cycle can improve eating event classification results. Six subjects participated in an experiment in a controlled setting consisting of both eating and non-eating events. Features were computed for each sensing measure to train a support vector machine model. This resulted in F1-scores of 0.82 for eating gestures, 0.94 for chewing food, and 0.58 for swallowing food.

A Custom-Tailored Multichannel Pressure Monitoring System Designed for Experimental Surgical Model of Abdominal Compartment Syndrome.

In experimental medicine, a wide variety of sensory measurements are used. One of these is real-time precision pressure measurement. For comparative studies of the complex pathophysiology and surgical management of abdominal compartment syndrome, a multichannel pressure measurement system is essential. An important aspect is that this multichannel pressure measurement system should be able to monitor the pressure conditions in different tissue layers, and compartments, under different settings. We created a 12-channel positive-negative sensor system for simultaneous detection of pressure conditions in the abdominal cavity, the intestines, and the circulatory system. The same pressure sensor was used with different measurement ranges. In this paper, we describe the device and major experiences, advantages, and disadvantages. The sensory systems are capable of real-time, variable frequency sampling and data collection. It is also important to note that the pressure measurement system should be able to measure pressure with high sensitivity, independently of the filling medium (gas, liquid). The multichannel pressure measurement system we developed was well suited for abdominal compartment syndrome experiments and provided data for optimizing the method of negative pressure wound management. The system is also suitable for direct blood pressure measurement, making it appropriate for use in additional experimental surgical models.

Evaluation of Ambient Sensor Systems for the Early Detection of Heart Failure Decompensation in Older Patients Living at Home Alone: Protocol for a Prospective Cohort Study.

The results of telemedicine intervention studies in patients with heart failure (HF) to reduce rehospitalization rate and mortality by early detection of HF decompensation are encouraging. However, the benefits are lower than expected. A possible reason for this could be the fact that vital signs, including blood pressure, heart rate, heart rhythm, and weight changes, may not be ideal indicators of the early stages of HF decompensation but are more sensitive for acute events triggered by ischemic episodes or rhythm disturbances. Preliminary results indicate a potential role of ambient sensor-derived digital biomarkers in this setting. The aim of this study is to identify changes in ambient sensor system-derived digital biomarkers with a high potential for early detection of HF decompensation. This is a prospective interventional cohort study. A total of 24 consecutive patients with HF aged 70 years and older, living alone, and hospitalized for HF decompensation will be included. Physical activity in the apartment and toilet visits are quantified using a commercially available, passive, infrared motion sensing system (DomoHealth SA). Heart rate, respiration rate, and toss-and-turns in bed are recorded by using a commercially available Emfit QS device (Emfit Ltd), which is a contact-free piezoelectric sensor placed under the participant's mattress. Sensor data are visualized on a dedicated dashboard for easy monitoring by health professionals. Digital biomarkers are evaluated for predefined signs of HF decompensation, including particularly decreased physical activity; time spent in bed; increasing numbers of toilet visits at night; and increasing heart rate, respiration rate, and motion in bed at night. When predefined changes in digital biomarkers occur, patients will be called in for clinical evaluation, and N-terminal pro b-type natriuretic peptide measurement (an increase of >30% considered as significant) will be performed. The sensitivity and specificity of the different biomarkers and their combinations for the detection of HF decompensation will be calculated. The study is in the data collection phase. Study recruitment started in February 2024. Data analysis is scheduled to start after all data are collected. As of manuscript submission, 5 patients have been recruited. Results are expected to be published by the end of 2025. The results of this study will add to the current knowledge about opportunities for telemedicine to monitor older patients with HF living at home alone by evaluating the potential of ambient sensor systems for this purpose. Timely recognition of HF decompensation could enable proactive management, potentially reducing health care costs associated with preventable emergency presentations or hospitalizations. ClinicalTrials.gov NCT06126848; https://clinicaltrials.gov/study/NCT06126848. PRR1-10.2196/55953.

Spatiotemporal features of traffic help reduce automatic accident detection time

Quick and reliable automatic detection of traffic accidents is of paramount importance to save human lives in transportation systems. However, automatically detecting when accidents occur has proven challenging, and minimizing the time to detect accidents (TTDA) by using traditional features in machine learning (ML) classifiers has plateaued. We hypothesize that accidents affect traffic farther from the accident location than previously reported. Therefore, leveraging traffic signatures from neighboring sensors that are adjacent to accidents should help improve their detection. We confirm this hypothesis by using verified ground-truth accident data, traffic data from radar detection system sensors, and light and weather conditions and show that we can minimize the TTDA while maximizing classification performance by considering spatiotemporal features of traffic. Specifically, we compare the performance of different ML classifiers (i.e, logistic regression, random forest, and XGBoost) when controlling for different numbers of neighboring sensors and TTDA horizons. We use data from interstates 75 and 24 in the metropolitan area that surrounds Chattanooga, TN. Our results show that the XGBoost classifier produces the best results by detecting accidents as quickly as 1.0 min after their occurrence with an area under the receiver operating characteristic curve of up to 83% and an average precision of up to 49%. We describe limitations, open challenges, and how the proposed framework can be used for quicker operational accident detection.

Multifunctional phthalocyanine NIR sensor for fluorescent and colorimetric dual-imaging and removal of intracellular and environmental Cu2+

Cu2+ sensing and removal are of great significance for environment protection and human health. Herein, multifunctional Cu2+ sensor system for both detection and removal of intracellular and environmental Cu2+ based on trans/cis interconversion of silicon phthalocyanine (SiPc) was developed. This phthalocyanine based Cu2+ sensor shows remarkably dual-mode NIR fluorescence and colorimetric responses for recognition of Cu2+, resulting in insoluble chelate for Cu2+ removal as well as the extremely high anti-interference in the presence of various anions and cations. The binding mechanism and stoichiometry ratio were systematically revealed. Intracellular fluorescence imaging in MCF-7 cells demonstrated that Cu2+ could be efficiently detected in living cells and form insoluble chelate for clearance. As the result, this novel phthalocyanine based multifunctional sensor can be used for dual-mode NIR fluorescence/colorimetric detection and removal of Cu2+ in either complicated environment or biological systems.

Effects of Temperature and Humidity on the Absorption Spectrum and Concentration of N2O Using an Open-Path Sensor System

This paper examines the effects of temperature and humidity on the absorption spectrum and concentration of nitrous oxide (N2O) using a compact, portable open optical-path gas detection sensor system. We obtained the absorption coefficient and widened the linear function of the N2O absorption spectrum related to temperature by theoretical analysis and the high-resolution transmission molecular absorption database (HITRAN). Afterward, we conducted real-time monitoring of N2O in both campus and laboratory environments using lasers for a duration of 32 h and 6 h, respectively, and the results were compared and analyzed with the theoretical derivation. The results show that the concentration of N2O increased with increasing environmental temperature but decreased with increasing humidity. Furthermore, the variations in temperature and humidity significantly affected the peak values of the second-harmonic (2f) and first-harmonic (1f) signals. Finally, the temperature N2O concentration and humidity N2O curves were calibrated separately, and temperature changes were positively correlated with the N2O concentration, while humidity changes were negatively correlated with the N2O concentration. The experimental results indicate that the concentration of N2O and its absorption spectra are influenced by humidity and temperature, which has a significant reference value in the absorption and measurement of N2O in practical applications.

A novel RCA-based DNA sensor system for specific and quantitative detection of Klebsiella pneumonia

The timely detection of Klebsiella pneumoniae (K. pneumoniae) infection is of great importance for patient management and the prevention of nosocomial infections. In this study, we present the development of a novel detection assay that enables sensitive and specific identification of K. pneumoniae-encoded topoisomerase IV (K. pneumoniae Topo IV) using a DNA sensor system in combination with Rolling Circle Amplification (RCA) technology. The DNA sensor system was designed by immobilizing DNA hairpin substrates on RCA primers modified slide surface. This modular reacting mode enhances the adaptability and flexibility of the sensor, thereby improving its universality as a sensing platform. When K. pneumoniae is present, the active Topo IV triggers the cleavage-ligation of DNA hairpin substrates, followed by RCA and labeling with fluorescent probes. This results in the visualization of K. pneumoniae at the single-molecule level by the DNA sensor, compared to situations when there are no K. pneumoniae present. The reliability and sensitivity of the DNA sensor were verified using purified enzymes and bacterial extracts, which establishes the foundation for quantitative detection of clinical samples. Notably, the sensor's detection limit for K. pneumoniae extracts was found to be 70 colony-forming units (CFU)/µL, a range considered clinically relevant for infections. Moreover, the DNA sensor system exhibited high sensitivity and accuracy in detecting K. pneumoniae from clinical samples, suggesting its potential application in clinical settings for detecting pathogenic microbes beyond K. pneumoniae.

Near-infrared wide-range dual-gas sensor system for simultaneous detection of methane and carbon monoxide in coal mine environment

To effectively monitor gas explosion and coal spontaneous combustion in coal mine, an intrinsically safe and explosion-proof dual-gas sensor system was developed for methane (CH4) and carbon monoxide (CO) measurement. A distributed feedback (DFB) laser was used to scan two adjacent absorption lines (CH4: 4284.51 cm−1, CO: 4285.01 cm−1). Two measurement ranges of 0–500 parts per million by volume (ppmv) and 0–300,000 ppmv for CH4 and 0–100 ppmv and 0–4000 ppmv for CO were realized using a ∼25 cm single-pass gas cell (SPGC) and a ∼25 m multi-pass gas cell (MPGC), respectively. A gas pre-treatment module was used to remove moisture and aerosols from gas samples. A temperature compensation algorithm was adopted to deal with ambient temperature change. Based on the Allan deviation analysis, the limit of detection (LoD) of CH4 is 0.19 ppmv and the LoD of CO is 0.12 ppmv, respectively, at an averaging time of 0.5 s. A continuous two-day CH4 measurement was performed to demonstrate the long-term stability and environmental monitoring capability. The sensor system was successfully deployed in coal mine gas analysis at Longjiapu, Changchun City, China.