Search Results (89)

Terahertz radiation exhibits significant potential for communications, imaging, and spectroscopy. However, the development of efficient and low-cost THz detectors remains challenging due to limitations such as insufficient sensitivity, slow response speed, and poor room temperature stability. This work presents an innovative quasi-2D perovskite/Weyl semimetal (Co₃Sn₂S₂) heterojunction THz detector that combines complementary material properties via band engineering. The device achieves a remarkable responsivity of 374.15 A/W, a specific detectivity of 6.27 × 10¹¹ cm·Hz^1/2·W⁻¹, and a noise-equivalent power of 0.29 pW·Hz^−1/2 at 0.1 THz. This performance stems from the strong THz absorption of the perovskite layer combined with the high carrier mobility and topological surface states of the Co₃Sn₂S₂, which collectively enable ultrafast carrier extraction and suppressed interfacial recombination. This heterojunction design offers a novel strategy for high-performance terahertz detection and facilitates its integration into next-generation portable, integrated devices. Full article

Despite many efforts to curtail drunk driving, alcohol-related traffic fatalities and injuries continue to be a major public health problem in the United States (U.S.) and most of the world. Technologies exist that prevent an automobile from starting if the driver’s breath alcohol exceeds 20 milligrams per deciliter (mg/dL), but these devices are only fitted to vehicles of individuals who have been convicted of Driving Under the Influence (DUI). A new approach must be taken to reduce the incidence of drunk driving by integrating an alcohol sensor system in vehicles as part of the delivered hardware. The system must be fast, accurate, and contactless—meaning that a forced exhalation is not required to measure the concentration of alcohol on the breath. We report on a novel device, the Driver Alcohol Detection System for Safety (DADSS) Breath Alcohol Sensor System, which uses the mid-infrared region of the electromagnetic spectrum to concurrently monitor alcohol and expired carbon dioxide (CO₂) to accurately quantify the breath alcohol concentration in samples that have been diluted in the atmosphere before being measured. The system was validated in a research laboratory with 70 male and female volunteers in 187 individual study days. Participants were given various doses of alcohol to consume and then breath and blood samples were collected simultaneously. Pearson correlation coefficients between the DADSS Breath Alcohol Sensor system and blood samples indicate a strong correlation between the measures, with an overall Pearson correlation of 0.8875 over an alcohol concentration range of 0–220 mg/dL. These results indicate that incorporating the DADSS system into motor vehicles has the potential to reduce the incidence of drunk driving. Full article

Giving consideration to cooking activity is important for sustainable housing. In contexts of limited ventilation, imposed by energy saving concerns, cooking causes deterioration of indoor air quality (IAQ) and occupants’ discomfort. This study presents a cooking event detection system that may support IAQ control to minimize the impact of cooking. The system consists of a multi-sensor device and a deep-learning neural network (DNN). The device monitors temperature (T), relative humidity (RH), suspended particulate matter (PM), CO₂, the responses of sensors to volatile organic compounds (VOCs), and other gases (NO₂, CO, CH₂O) in the kitchen zone. The collected data are processed by the DNN. The detection system generates a response every 7 s, indicating either ’COOKING’ or ’NO COOKING’. Feature vector selection was based on classification performance and cost considerations. Cooking event misdetections generate unjustified IAQ control costs: economic ones (UEC), when the system detects a non-existent event, and environmental ones (UEN), when the system fails to detect an actual event. In this study, several well-performing detection systems were developed, with miss rates ranging from 5.1% to 20.5% and false detection rates ranging from 7.7% to 11.7%. The results show that gas sensor responses—particularly to VOCs—had greater utility for cooking event detection compared with T, RH, CO₂, and PM. The cost analysis demonstrated that IAQ control supported by the developed cooking event detection systems could generate higher total unjustified environmental costs when the unit cost ratio UEN/UEC exceeded 1.25, or higher total unjustified economic costs when the unit cost ratio UEN/UEC was below 1.43. We believe this work will contribute to the development of novel automatic IAQ control systems supported by event detection. Full article

AI-driven network security relies increasingly on Large Language Models (LLMs) to detect sophisticated threats; however, their deployment on resource-constrained edge devices is severely hindered by immense parameter scales. While unstructured pruning offers a theoretical reduction in model size, commodity Graphics Processing Unit (GPU) architectures fail to efficiently leverage element-wise sparsity due to the mismatch between fine-grained pruning patterns and the coarse-grained parallelism of Tensor Cores, leading to latency bottlenecks that compromise real-time analysis of high-volume security telemetry. To bridge this gap, we propose SPARTA (Sparse Parallel Architecture for Real-Time Threat Analysis), an algorithm–architecture co-design framework. Specifically, we integrate a hardware-based address remapping interface to enable flexible row-offset access. This mechanism facilitates a novel graph-based column vector merging strategy that aligns sparse data with Tensor Core parallelism, complemented by a pipelined execution scheme to mask decoding latencies. Evaluations on Llama2-7B and Llama2-13B benchmarks demonstrate that SPARTA achieves an average speedup of 2.35× compared to Flash-LLM, with peak speedups reaching 5.05×. These findings indicate that hardware-aware microarchitectural adaptations can effectively mitigate the penalties of unstructured sparsity, providing a viable pathway for efficient deployment in resource-constrained edge security. Full article

In industrial combustion processes, high concentrations of propane (C₃H₈) gas are employed. Therefore, developing gas-detecting devices that operate under high concentrations, elevated temperatures, and short response times is crucial. This paper presents the design, simulation, and construction of a novel propane (C₃H₈) gas detector. The design was based on the dynamic electrical response of a gas sensor fabricated with cobalt antimoniate (CoSb₂O₆). The simulation considered the device structure and programming criteria, and the final prototype was constructed according to the sensor response, design parameters, and operating principles. Design, simulation, and fabrication results were in concordance, confirming the correct operation of the detector at high gas concentrations. A mathematical model was derived from the sensor’s electrical response, establishing a resistance value that allowed a two-second response time. This resistance was used to adapt the signal between the gas sensor and the PIC18F2550 microcontroller. Input/output signals, safety criteria, and functionality principles were considered in the programming device. The resulting propane (C₃H₈) gas detector operates at 300 °C, detects high C₃H₈ concentrations, and achieves a 2 s response time, making it ideal for industrial applications where combustion monitoring is essential. Full article

Cancer remains a leading cause of mortality worldwide. Chemotherapy is effective but can cause febrile neutropenia, deplete neutrophils, and increase infection risk. Although timely fever detection is critical to prevent complications, periodic temperature monitoring has inherent limitations. Wearable devices (WDs) for real-time body temperature (RBT) monitoring are emerging as tools for early fever detection, though their clinical feasibility and usability remain unverified. This study assessed the clinical feasibility of a novel WD by comparing its temperature concordance and fever detection capability against a standard reference in patients with cancer. The RBT system’s clinical maturity, including its capacity for early fever detection and monitoring treatment-related changes, was evaluated. Furthermore, satisfaction among clinicians and patients was analyzed in a hospital setting. In this prospective study, patients with hematologic malignancies at high infection risk admitted to the Chonnam National University in Hwasun Hospital, South Korea, were enrolled. Data from 47 patients were analyzed to compare MT100D as WD (SEERS Technology Co., Ltd., South Korea) readings with axillary thermometers. Temperature concordance was evaluated using the intraclass correlation coefficient (ICC), and diagnostic performance was compared with the reference thermometer. The early fever detection performance of the MT100D was analyzed to evaluate the clinical effectiveness of the early response system. Usability was evaluated through a five-point Likert scale survey completed by clinical nurses. The MT100D demonstrated good agreement with the reference thermometer (mean difference: −0.19 ± 0.35 °C; ICC: 0.78). The RBT system achieved sensitivity, specificity, and area under the receiver operating characteristic curve values of 81.50%, 96.32%, and 96.60%, respectively. The early fever detection rate was 77.1%, with the RBT system detecting fever an average of 1.13 ± 1.28 h earlier than the reference. Usability and satisfaction assessments showed high patient satisfaction (mean 4.69 ± 0.5; range 4.57–4.80) and moderate clinician satisfaction (mean 3.63 ± 0.75; range 2.89–4.11). Among patients with cancer, including those at risk of febrile neutropenia, the MT100D demonstrated strong concordance with standard thermometry, enabling early fever detection. The RBT system shows promise for the early identification of neutropenic risk during chemotherapy. Full article

The detection of volatile organic compounds (VOCs) at low operating temperatures is critical for public health, environmental monitoring, and industrial safety, yet it remains a significant challenge for conventional sensor technologies. Metal-organic frameworks (MOFs) have emerged as highly versatile precursors for creating advanced sensing materials. This review critically examines the transformation of MOFs into functional catalytic interfaces for low-temperature chemiresistive VOC sensing. We survey the key synthetic strategies, with a focus on controlled pyrolysis, that enable the conversion of insulating MOF precursors into semiconducting derivatives with tailored porosity, morphology, and catalytically active sites. This review establishes the crucial synthesis-structure-performance relationships that govern sensing behavior, analyzing how factors like calcination temperature and precursor composition dictate the final material’s properties. We delve into the underlying chemiresistive sensing mechanisms, supported by evidence from advanced characterization techniques such as in situ DRIFTS and density functional theory (DFT) calculations, which elucidate the role of oxygen vacancies and heterojunctions in enhancing low-temperature catalytic activity. A central focus is placed on the persistent challenges of achieving high selectivity and robust performance in complex, real-world environments. We critically evaluate and compare strategies to mitigate interference from confounding gases and ambient humidity, including intrinsic material design and extrinsic system-level solutions like sensor arrays coupled with machine learning. Finally, this review synthesizes the current state of the art, identifies key bottlenecks related to stability and scalability, and provides a forward-looking perspective on emerging frontiers, including novel device architectures and computational co-design, to guide the future development of practical MOF-derived VOC sensors. Full article

Digital Mobility outcomes can serve as objective biomarkers of health, but their validation in populations with multiple long-term conditions (MLTCs) based on wrist-worn devices remains unexplored. We refined, improved, and introduced novel algorithms, specifically tailored and adapted for (i) gait sequence detection, (ii) initial contact identification, and (iii) stride length estimation from a single wrist-worn device. Validation was performed using data from 28 participants with co-occurring MLTCs performing a 2.5 h real-world monitoring session. Reference data from an established multi-sensor system were used to assess algorithm performance across diverse gait patterns of co-occurring MLTCs. Twenty-eight participants (mean age 70.4 years, 43% females) had a median of three co-occurring MLTCs. Among six gait sequence detection methods, improved versions of the Kheirkhahan algorithm performed best (accuracy = 0.92, specificity = 0.96). For initial contact detection (nine methods tested), Shin’s algorithm achieved the highest performance index (0.85) followed by McCamley (0.84). Stride length estimation was most accurate using novel approaches based on the Weinberg method (performance index > 0.70). The proposed fine-tuned algorithms, the newly developed adaptive variants, and the foot-length augmented versions demonstrated robust performance, surpassing many existing methods and addressing the complexity of gait patterns in MLTCs. These findings enable scalable, real-time mobility monitoring in complex clinical populations using accessible wearable technology. Full article

Pomegranate (Punica granatum) fruit size estimation plays a crucial role in orchard management decision-making, especially for fruit quality assessment and yield prediction. Currently, fruit sizing for pomegranates is performed manually using calipers to measure equatorial and polar diameters. These methods rely on human judgment for sample selection, they are labor-intensive, and prone to errors. In this work, a novel framework for automated on-tree detection and sizing of pomegranate fruits by a farmer robot equipped with a consumer-grade RGB-D sensing device is presented. The proposed system features a multi-stage transfer learning approach to segment fruits in RGB images. Segmentation results from each image are projected on the co-located depth image; then, a fruit clustering and modeling algorithm using visual and depth information is implemented for fruit size estimation. Field tests carried out in a commercial orchard are presented for 96 pomegranate fruit samples, showing that the proposed approach allows for accurate fruit size estimation with an average discrepancy with respect to caliper measures of about 1.0 cm on both the polar and equatorial diameter. Full article

The National Institute of Earth Physics (NIEP) in Romania has upgraded its seismic monitoring stations into multifunctional platforms equipped with advanced devices for measuring gas emissions, magnetic fields, telluric fields, solar radiation, and more. This enhancement enabled the integration of a seismic forecasting system designed to extend the alert time of the existing warning system, which previously relied solely on seismic data. The implementation of an Operational Earthquake Forecast (OEF) aims to expand NIEP’s existing Rapid Earthquake Early Warning System (REWS) which currently provides a warning time of 25–30 s before an earthquake originating in the Vrancea region reaches Bucharest. The AFROS project (PCE119/4.01.2021) introduced fundamental research essential to the development of the OEF system. As a result, real-time analyses of radon and CO₂ emissions are now publicly available at afros.infp.ro, dategeofizice. The primary monitored area is Vrancea, known for producing the most destructive earthquakes in Romania, with impacts extending to neighboring countries such as Bulgaria, Ukraine, and Moldova. The structure and methodology of the monitoring network are adaptable to other seismic regions, depending on their specific characteristics. All collected data are stored in an open-access database available in real time, geobs.infp.ro. The monitoring methods include threshold-based event detection and seismic data analysis. Each method involves specific technical nuances that distinguish this monitoring network as a novel approach in the field. In conclusion, experimental results indicate that the Gutenberg-Richter law, combined with gas emission measurements (radon and CO₂), can be used for real-time earthquake forecasting. This approach provides warning times ranging from several hours to a few days, with results made publicly accessible. Another key finding from several years of real-time monitoring is that the value of fundamental research lies in its practical application through cost-effective and easily implementable solutions—including equipment, maintenance, monitoring, and data analysis software. Full article

Soil organic matter (SOM) content is a key indicator for assessing soil health, carbon cycling, and soil degradation. Traditional SOM detection methods are complex and time-consuming and do not meet the modern agricultural demand for rapid, non-destructive analysis. While significant progress has been made in spectral inversion for SOM prediction, its accuracy still lags behind traditional chemical methods. This study proposes a novel approach to predict SOM content by integrating spectral, texture, and color features using a three-branch convolutional neural network (3B-CNN). Spectral reflectance data (400–1000 nm) were collected using a portable hyperspectral imaging device. The top 15 spectral bands with the highest correlation were selected from 260 spectral bands using the Correlation Coefficient Method (CCM), Boruta algorithm, and Successive Projections Algorithm (SPA). Compared to other methods, CCM demonstrated superior dimensionality reduction performance, retaining bands highly correlated with SOM, which laid a solid foundation for multi-source data fusion. Additionally, six soil texture features were extracted from soil images taken with a smartphone using the gray-level co-occurrence matrix (GLCM), and twelve color features were obtained through the color histogram. These multi-source features were fused via trilinear pooling. The results showed that the 3B-CNN model, integrating multi-source data, performed exceptionally well in SOM prediction, with an R² of 0.87 and an RMSE of 1.68, a 23% improvement in R² compared to the 1D-CNN model using only spectral data. Incorporating multi-source data into traditional machine learning models (SVM, RF, and PLS) also improved prediction accuracy, with R² improvements ranging from 4% to 11%. This study demonstrates the potential of multi-source data fusion in accurately predicting SOM content, enabling rapid assessment at the field scale and providing a scientific basis for precision fertilization and agricultural management. Full article

This study introduces a method aimed at enhancing both the accuracy and the range of magnetic field sensors, which are two critical parameters, in a novel NiCo₂O₄-based anomalous Hall effect sensor. To fine-tune the linear range of the sensor, we introduced epitaxial strain using a MgAl₂O₄ cover layer, which significantly influenced the strain-modulated magnetic anisotropy. A NiCo₂O₄/MgAl₂O₄/NiCo₂O₄/MgAl₂O₄ heterostructure was further constructed, achieving differentiation in the material characteristics across both upper and lower NiCo₂O₄ layers through the modulation of thickness and strain. A dual-layer Hall bar was designed to enhance the integration of the sensor, offering varied detection ranges. This approach enabled the realization of ultrahigh sensitivity, measuring 10,000 V/(AT) within a ±0.1 mT range, and a competitive sensitivity of 60 V/(AT) within a ±5 mT range. By reducing the thickness of the top NiCo₂O₄ layer, an ultra-wide measurement range of ±1000 mT was also achieved. These results highlight the considerable promise of NiCo₂O₄-based anomalous Hall effect devices as compact, multi-range tools in the domain of magnetic sensing technology. Full article

Biosensing shows promise in detecting cancer, renal disease, and other illnesses. Depending on their transducing processes, varieties of biosensors can be divided into electrochemical, optical, piezoelectric, and thermal biosensors. Advancements in material production techniques, enzyme/protein designing, and immobilization/conjugation approaches can yield novel nanoparticles with further developed functionality. Research in cutting-edge biosensing with multifunctional nanomaterials, and the advancement of practical biochip plans utilizing nano-based sensing material, are of current interest. The miniaturization of electronic devices has enabled the growth of ultracompact, compassionate, rapid, and low-cost sensing technologies. Some sensors can recognize analytes at the molecule, particle, and single biological cell levels. Nanomaterial-based sensors, which can be used for biosensing quickly and precisely, can replace toxic materials in real-time diagnostics. Many metal-based NPs and nanocomposites are favorable for biosensing. Through direct and indirect labeling, metal-oxide NPs are extensively employed in detecting metabolic disorders, such as cancer, diabetes, and kidney-disease biomarkers based on electrochemical, optical, and magnetic readouts. The present review focused on recent developments across multiple biosensing modalities using metal/metal-oxide-based NPs; in particular, we highlighted the specific advancements of biosensing of key nanomaterials like ZnO, CeO₂, and TiO₂ and their applications in disease diagnostics and environmental monitoring. For example, ZnO-based biosensors recognize uric acid, glucose, cholesterol, dopamine, and DNA; TiO₂ is utilized for SARS-CoV-19; and CeO₂ for glucose detection. Full article

Biosensors are transforming point-of-care diagnostics by simplifying the detection process and enabling rapid, accurate testing. This study introduces a novel, reusable biosensor designed for direct viral RNA detection from unfiltered saliva, targeting SARS-CoV-2. Unlike conventional methods requiring filtration, our biosensor leverages a unique electrode design that prevents interference from saliva debris, allowing precise measurements. The biosensor is based on electrochemical principles, employing oligonucleotide probes immobilized on a hydrophobic-coated electrode, which prevents air bubbles and salt crystal formation. During validation, the biosensor demonstrated a sensitivity and specificity of 100%, accurately identifying SARS-CoV-2 in saliva samples without false positives or negatives. Cross-validation with RT-qPCR, the gold standard for COVID-19 diagnostics, confirmed the reliability of our device. The biosensor’s performance was tested on 60 participants, yielding 12 true positive results and 48 true negatives, aligning perfectly with RT-qPCR outcomes. This reusable, easy-to-use biosensor offers significant potential for point-of-care applications in various healthcare settings, providing a fast, efficient, and cost-effective method for detecting viral infections such as COVID-19. Its robust design, minimal sample preparation requirements, and multiple-use capability mark a significant advancement in biosensing technology. Full article

Biosensors harness biological materials as receptors linked to transducers, enabling the capture and transformation of primary biorecognition signals into measurable outputs. This study presents a novel carboxylation method for synthesizing carboxylated graphene (CG) under acidic conditions, enhancing biosensing capabilities. The characterization of the CG was performed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Raman spectroscopy, thermogravimetric analysis (TGA), and X-ray diffraction (XRD). We modified screen-printed carbon electrodes (SPCEs) with CG to immobilize the SARS-CoV-2 N-protein, facilitating targeted detection of IgA antibodies (IgA-SARS-CoV-2). The analytical performance was assessed via electrochemical techniques such as cyclic voltammetry and electrochemical impedance spectroscopy, confirming CG synthesis effectiveness and biosensor functionality. The developed biosensor efficiently detects IgA-SARS-CoV-2 across a dilution range of 1:1000 to 1:200 v/v in a phosphate-buffered saline (PBS) solution, with a limit of detection calculated at 1:1601 v/v. This device shows considerable potential because of its fast response time, miniaturized design facilitated by SPCEs, reduced sample volume requirements, high sensitivity and specificity, low detection limits, and signal enhancement achieved through nanomaterial integration. Full article