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A highly sensitive sensor based on carbon fiber paper modified with a shungite–copper formazanate composite is presented for the voltammetric determination of lidocaine. The synthesized organometallic complex, composite, and modified electrode are characterized by infrared spectroscopy, high-resolution mass spectrometry, elemental analysis, scanning electron microscopy, and cyclic and linear sweep voltammetry. The twofold increase in the current of the lidocaine oxidation peak on the modified electrode compared to the unmodified one is associated with the sensitizing effect of the composite modifier, which is due to an increase in the electroactive area and the number of lidocaine binding sites on the electrode surface. The sensor exhibits a wide dynamic range from 2 to 2120 µM with a low limit of detection of 0.18 µM lidocaine and high sensitivity of 0.755 µA/V µM. The interelectrode and intraelectrode repeatability of the analytical signal do not exceed 3.5%. The sensor response is stable within three weeks. The developed sensor was used for the determination of lidocaine in pharmaceuticals. The results of an analysis of real samples demonstrated good reproducibility (RSD ≤ 5.5%) and recovery (98–102%). [ABSTRACT FROM AUTHOR]

A user-friendly, cost-effectively, portable, and environmentally friendly colorimetric sensor for the quantitative determination of formaldehyde was developed based on the combining of microfluidic paper-based analytical device (μPAD), headspace microextraction (HSME), and digital image colorimetry. Coupling HSME and μPAD led to enhancements in selectivity and sensitivity of the sensor through sample cleanup and analyte enrichment. To construct the μPAD-HSME device, two pieces of paper as the sample and detection zone were placed facing each other so that a small common and sealed space was created between them. The color change occurred when the analyte in the gaseous form crossed this gap and reached the detection zone. Colorimetric sensing in the detection zone was performed based on the Hantzsch reaction. The color change in the detection zone was recorded by a smartphone and digital images were processed using image analysis software based on the RGB model. The influence of some key variables on the sensitivity of the method including derivatization reagent composition, sample volume, extraction temperature, and extraction time was studied and optimized. The linear dynamic range of the method was obtained in two ranges of 0.10–0.75 and 0.75–5.0 mg L−1 with a limit of detection of 0.03 mg L−1. The recoveries were in the range 80–126% for the quantification of formaldehyde in textile, milk, and wastewater samples. [ABSTRACT FROM AUTHOR]

Paper-based electronics have attracted much attention due to their softness, degradability, and low cost. However, paper-based sensors are difficult to apply to high-humidity environments or even underwater. Here, we report a fully paper-integrated piezoresistive sensing system that exhibits flexibility, waterproofing, air permeability, and biocompatibility. This system consists of hydrophobic paper as the substrate and encapsulation layer, conductive paper with a double 'zig-zag' and dotted surface structure as the sensing layer, and silver paste films as the interconnects. The structural design of the sensing layer helps to increase the contact area in adjacent layers under pressure and further improves the pressure sensitivity. The piezoresistive system can be worn on human skin in the ambient environment, wet environment, and water for real-time monitoring of physiological signals with air permeability and waterproofing due to its hydrophobic fiber structure. Such a device provides a reliable, economical, and eco-friendly solution to wearable technologies. [ABSTRACT FROM AUTHOR]

A highly stable and luminescent lead-free manganese(II) halide hybrid MnBr4(TMN)2 (C34H42Br4MnN4) was designed and synthesized by introducing a large cationic organic spacer. The MnBr4(TMN)2 displays high luminescence with quantum yields up to 77% and possesses turn-off fluorescence behavior (Ex/Em=365/546 nm) for water. These properties make the MnBr4(TMN)2 a promising candidate as an alternative indicator for the detection of water with potential applications for the fabrication of LEDs. Herein, a paper-based sensor based on MnBr4(TMN)2 is described for the determination of water content in organic solvents. The mechanism of water sensing can be tentatively explained by fluorescence quenching originating from the destruction of water due to the Mn–Br bonds of MnBr4(TMN)2. The MnBr4(TMN)2-based paper sensor exhibits an excellent discrimination ability of water content in the range 0–25.0% with a detection limit of 0.27%. Satisfactory recoveries (94.91±4.09% to 103.23±2.38%) are obtained in spiked ethanol solvent samples, which demonstrate that the MnBr4(TMN)2-based paper sensor is capable of detecting water content in real ethanol solvent samples. [ABSTRACT FROM AUTHOR]

Nowadays, in the clinical, pharmaceutical, and environmental sectors, the development of facile and sensitive analytical methods and/or innovative devices for the follow-up and detection of antibiotics and pharmaceutical formulations, in general, are urgently needed and still challenging. This work declared three vital applications for broad-spectrum nitrofurantoin (macrofuran) antibiotic detection and quantification: A colorimetric method, a coated paper strip-based nano-lanthanum complex prototype and fabrication of smart electronic color sensor device-based coated paper strips. The colorimetric method showed a significant response upon increasing the concentration of the nitrofurantoin in a range between (1.0–100.0 ng/mL) via a visual color change from orange-yellow to red colors degree with detection and quantification limits of 0.175 and 0.53 ng/mL, respectively, whereas the nano-lanthanum complex coated paper strip prototype showed qualitative on-site sensing for nitrofurantoin via naked eye color changes which can be detected anywhere. Moreover, a smart prototype for detecting macrofuran in the means of paper color change in the RGB color component extraction algorithm and the grayscale projection value processing algorithm was fabricated. The change in RGB color on the coated paper strip was detected using an electronic color sensor device. The developed colorimetric method, coated paper strip, and the electronic color sensor device prototype exhibited fast, simple, costless, and selective towards macrofuran over the competing analyzed. As well as, showed good applicability in the different real samples spiked with different concentrations of macrofuran. [ABSTRACT FROM AUTHOR]

Paper-based devices have been very much in the foreground of analytical science recently. This work innovatively proposed a fluorescent paper–based sensor (FPS) constructed on a hybrid polydimethylsiloxane (PDMS)/paper platform where cellulose papers functionalized with carbon dots (CDs) as fluorophores by Schiff base chemistry were loaded on the grooves array of a designed PDMS plate. As a proof of concept, the performance of FPS was investigated with folic acid (FA) as the target analyte. Under optimal conditions, FPS enabled a rapid fluorescence quenching response to FA via inner filter effect in a wide range of 1–300 μmol L−1 with the limit of detection of 0.28 μmol L−1. The feasibility of FPS was further verified by the detection of FA in orange juice and urine samples with satisfactory results. The covalent modification of CDs on paper endowed the FPS with good assay reproducibility and stability. Interestingly, FPS achieved a more sensitive assay of FA than the conventional strategy, by which the same CDs were directly used to detect FA in a solution-based system. The FPS illuminated a novel strategy for construction of reliable and sensitive assays based on paper-based devices. It is of paramount importance for its practical application in biosensing and clinical diagnosis. [ABSTRACT FROM AUTHOR]

Sulfite is a common food additive that prevents oxidation from damaging food nutrients, and it has long been used in the food industry as a bleaching agent. It can harm the human body if taken wrongly or excessively. In this study, three dyes (cresol red, chlorophenol red, and bromocresol green) were explored to analyze the presence of sodium sulfite (SS) in an inexpensive, disposable paper sensor with a lower visible detection limit of 0.05 M. This visual paper sensor detects sodium sulfite with high selectivity and sensitivity at room temperature. An IoT-based sensor was also developed to practically apply the developed method, which is rapid and low-cost and can replace heavy-duty instruments. Both these sensors can substantially impact scenarios such as food quality monitoring and detecting sodium sulfite in medicinal items. [ABSTRACT FROM AUTHOR]

The adverse effect of ultraviolet (UV) radiation on human beings has sparked intense interest in the development of new sensors to effectively monitor UV and solar exposure. This paper describes a novel low-cost and flexible graphene oxide (GO)-based paper sensor capable of detecting the total amount of UV or sun energy delivered per unit area. GO is incorporated into the structure of standard printing paper, cellulose, via a low-cost fabrication technique. The effect of UV and solar radiation exposure on the GO paper-based sensor is investigated using a simple color change analysis. As a result, users can easily determine the amount of ultraviolet or solar energy received by the sensor using a simple color analysis application. A neural network (ANN) model is also explored to learn the relation between UV color intensity and exposure time, then digitally display the results. The accuracy for the developed ANN reached 96.83%. The disposable, cost-effective, simple, biodegradable, safe, and flexible characteristics of the paper-based UV sensor make it an attractive candidate for a variety of sensing applications. This work provides new vision toward developing highly efficient and fully disposable GO-based photosensors. [ABSTRACT FROM AUTHOR]

A challenge for paper-based cation sensors relying on classical carrier-based ion-selective optodes (ISOs) is their pH-cross response caused by the use of H+-sensitive chromoionophores as optical signal transducers. This work demonstrates fully pH-independent fluorescence-based calcium detection with a paper-based plasticizer-free ISO. To achieve a pH-independent assay, a solvatochromic dye (SD) instead of a traditional H+-sensitive chromoionophore has been applied to the paper-based ISO by means of inkjet printing technology. The detection principle depends on an ionophore-driven phase-transfer ion-exchange reaction between target cations and the positively charged SD, which no longer involves H+ in the optical signal transduction process. The developed paper-based ISOs with the SD resulted in Ca2+ concentration-dependent response curves not affected by the sample pH (pH 6.0, 7.0, and 8.0). The dynamic range obtained for Ca2+ detection was from 10−5 to 1 mol L−1 with a detection limit of 19.3 μmol L−1. Additionally, excellent selectivity derived from the used ionophore has been confirmed. As a simple practical application, the determination of Ca2+ in mineral water has been achieved without the pH-buffering process required for conventional cation-exchange ISOs. [ABSTRACT FROM AUTHOR]

Bacillus cereus is one of the most common foodborne pathogens found in various kinds of staple foods such as rice and wheat. A rapid and accurate detection method for this pathogen is highly desirable for the sustainable production of relevant food products. While several classical and molecular-based detection methods are available for the identification of B. cereus, they suffered one or more limitations such as the requirement for a tedious and time-consuming process, less than ideal specificity, and the lack of portability. Herein, we developed the first paper-based sensing device that exhibits high species specificity with sufficiently low limit of detection for the visual detection of specific DNA sequences of B. cereus. The success is attributed to the strategic planning of fabrication in various dimensions including thorough bioinformatics search for highly specific genes, the use of the pyrrolidinyl peptide nucleic acid (PNA) probe whose selectivity advantage is well documented, and an effective PNA immobilization and DNA-binding visualization method with an internal cross-checking system for validating the results. Testing in rice matrices indicates that the sensor is capable of detecting and distinguishing B. cereus from other bacterial species. Hence, this paper-based sensor has potential to be adopted as a practical means to detect B. cereus in food industries. [ABSTRACT FROM AUTHOR]

N, Cl-doped carbon dots (N, Cl-CDs) were prepared by hydrothermal method from rhodamine B (RhB) and ethylenediamine (EDA). The resulting N, Cl-CDs exhibited fascinating solvent dependence and strict excitation independence. As the polarity of the solvent increased (from tetrahydrofuran (THF) to water), the emission spectrum of N, Cl-CDs was redshifted and the fluorescence efficiency decreased, which were attributed to hydrogen bond-induced aggregation. Taking advantage of these attributes, the N, Cl-CDs were used as suitable probes for fluorescence and colorimetric dual-mode detection of water in THF. The linear relationship was 0.5–100% water with the detection limit down to 0.093%. Moreover, the sensing platform was converted into a paper-based sensor for handy, real-time, and visible humidity sensing. N, Cl-CDs/PVA films were fabricated and realized continuously tunable solid-state fluorescence, further expanding their practical application. [ABSTRACT FROM AUTHOR]

As a human-computer interaction technology for the Internet of Things (IoT), non-contact humidity sensors have been widely explored. However, traditional humidity sensors have become a bottleneck in the development of flexible electronics owing to their complex processes and non-degradability. Here, bacterial cellulose (BC) and MWCNT were sequentially coated on paper to construct a durable and highly sensitive bacterial cellulose/MWCNT humidity sensor (BCNT). The BC endows the paper substrate with dense structure and significantly reduces the consumption of active materials (6.7 times reduction). Further, the well-dispersed CNT coating forms an interlocking structure with the BC nano-network, which results in a high mechanical strength (66.3 MPa). The maximum response of BCNT02 can be up to 94.5% (− ΔI/I0) under 98% RH, and the response and recovery times were 150 and 297 s, respectively. Moreover, the high humidity sensitivity of the BCNT make it an excellent choice for research and development of humidity sensors for respiration detection, non-contact switching and humidity localization. [ABSTRACT FROM AUTHOR]

The copper(II) ion (Cu2+) has played an indispensable role in diverse kinds of functional physiological processes of organisms, which has become of growing interest. Despite the fact that numerous Cu2+ test papers using fluorescent probes have been fabricated, sensors featuring the ratiometric property that integrates quenched probes and an inner standard dye are rarely reported. Herein, a two-component ratiometric sensor in a paper-based device is proposed to realize highly selective Cu2+ detection. To overcome shortcomings such as low signal-to-noise ratio and incorrect response of the quenching probe, a novel BODIPY-based turn-off probe (P2017) is designed and introduced into the paper-based device with better water solubility and selectivity for Cu2+ detection. Furthermore, a reference dye (B001), exhibiting an emission at 690 nm when the excitation wavelength is 480 nm, is also introduced into the paper-based device. These two components can enhance the quality of the signal as P2017 is sensitively quenched by Cu2+, while B001 with a photostable property, serving as an internal benchmark, is unable to react with Cu2+. The results indicated that the two components provided a new concept for optimizing paper-based device fabrication and developing accurate, simple, and inexpensive Cu2+ detection methods, which could be potentially applied to monitor human health and the environment in remote areas. [ABSTRACT FROM AUTHOR]

The microstructure plays an important role in improving the sensing performance of pressure sensor. However, the design of microstructural active layer of pressure sensor usually involves complex process and expensive raw materials. Herein, the common polyester conductive electrodes and cellulose paper that both have inherent microstructure surface are combined to form two-sided microstructure interfaces for low-cost, eco-friendly and high-performance flexible piezoresistive pressure sensor. In order to obtain conductive and low-cost active layer paper, daily carbon ink, which is usually used for writing, is preferred as a conductive material. Meanwhile, we experimentally confirm that the proposed structure is also suitable for other conductive materials, such as carbon nanotubes. The results show that as-fabricated piezoresistive sensor has high pressure sensitivities of 5.54 and 1.61 kPa−1 in the wide linear ranges of 0.5 − 5 and 5 − 60 kPa, respectively, and good durability (5000 cycles under 2 kPa). The sensing mechanism of the piezoresistive sensor is analyzed by combining the characterization results and finite element simulation. Benefitting from the high sensing performance and good flexibility, the piezoresistive sensor is demonstrated for multiple wearable applications (e.g., wrist pulse, speech recognition, finger bending, abdominal respiration, counting steps, and pressure distribution). This work provides a simple and effective strategy for the design of piezoresistive sensor from the microstructure interfaces between electrodes and active layer. [ABSTRACT FROM AUTHOR]

A novel donor–acceptor fluorescent sensor was designed and synthesized. The sensor exhibited high selectivity and sensitivity to Zn2+ in acetonitrile solution. When 3.0 equiv. of Zn2+ was added gradually, the emission intensity at 500 nm increased 54-fold, accompanied by the fluorescent color of the solution changed from dark to green. Job's plot and ESI-MS were carried out to verify a 1:1 stoichiometric complex was formed between the sensor and Zn2+. The limit of detection (LOD) to Zn2+ was measured to be 2.81 × 10−9 mol L−1. Moreover, the sensor not only could be used to detect Zn2+ in practical water samples with high accuracy, but also could be made into test paper for the qualitative detection for Zn2+. [ABSTRACT FROM AUTHOR]

Importantly, trends and developments of this interaction found in both biosensors and bioassays have opened new perspectives in the sensing field and are therefore also of great interest to the sensor community. (i) Biosensors are chemical sensors in which the recognition system utilizes a biochemical mechanism for the interaction process in/at the recognition layer. [Extracted from the article]

The condensation product (L) of 4,4′-methylenedianiline and p-anisaldehyde acts as colorimetric sensor for Cu2+ and Pb2+ ions. On interaction with Cu2+, ethanolic solution of L changes its color to brown while it becomes light pink on interaction with Pb2+. Interaction of Al3+ with L coated paper strip emits bright blue fluorescence. Metal ions like Mg2+, Cu2+, Li+, K+, Na+, Mn2+, Al3+, Hg2+, Co2+, Pb2+, Ni2+, Cd2+, Zn2+, Fe3+ do not interfere the paper strip sensor. The fluorescent intensity of L in ethanol is quenched 25 times by Pb2+ ion. The interaction between L and Pb2+ is reversible and the detection limit of Pb2+ is 10−6 M. The binding constant and stoichiometry of binding between L and Pb2+ was calculated to be 104.8 and 1:2. Theoretical calculations show that the binding of the metal ions to L are favorable and the fluorescence of L is due to π → π* transition. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerators (TENGs) offer an emerging market of self-sufficient power sources, converting the mechanical energy of the environment to electricity. Recently reported high power densities for the TENGs provide new applications opportunities, such as self-powered sensors. Here in this research, a flexible graphene oxide (GO) paper was fabricated through a straightforward method and utilized as the electrode of TENGs. Outstanding power density as high as 1.3 W.m−2, an open-circuit voltage up to 870 V, and a current density of 1.4 µA.cm−2 has been extracted in vertical contact-separation mode. The all-flexible TENG has been employed as a self-powered humidity sensor to investigate the effect of raising humidity on the output voltage and current by applying mechanical agitation in two forms of using a tapping device and finger tapping. Due to the presence of superficial functional groups on the GO paper, water molecules are inclined to be adsorbed, resulting in a considerable reduction in both generated voltage (from 144 V to 14 V) and current (from 23 µA to 3.7 µA) within the range of relative humidity of 20% to 99%. These results provide a promising applicability of the first suggested sensitive self-powered GO TENG humidity sensor in portable/wearable electronics. [ABSTRACT FROM AUTHOR]

Herein, we introduce a low-cost and straightforward method for preparing pH sensors based on parchment-type paper and pencil traces as a base and electrode, respectively. The hydrophobic feature of the parchment paper supports the relatively long life of the proposed pH sensor. The results indicate that the present sensors have a Nernstian response with a Nernstian slope of 52.1 ± 1.5 mV/pH and a good linearity of 0.995 from pH 4 to 10. The stability of the proposed electrode is approximately 0.75 mV/h. The prepared electrodes show a relatively long operating life compared with the published results of ordinary paper-based pH devices. However, the Nernstian factor decreases over time and stabilizes at approximately 28 mV/pH after two months. This approach provides a low-cost and reproducible pH sensor. [ABSTRACT FROM AUTHOR]

Flexible sensors have recently attracted much attention for potential applications in motion measurement and wearable health monitoring. In this paper, an environment-friendly, economic, and high-performance paper-based self-inductive folding displacement sensor (PSIFS) was proposed, with displacement resolution of 20 μm and wide measurement range of 43.2 mm. The sensor sensitivity could reach 4.44% mm−1 with the maximum deviation of 0.00904 in experiments. The sensing mechanism based on inductance variation due to three-dimensional deformation of planar inductor coil and structural design principle under two dimensions of sensitivity and size were proposed and studied. The folding method to enhance sensitivity was proposed and 3 times sensitivity enhancement could be achieved by once folding. Multiple breathing states and motion states of the human joints could be recognized. The proposed simple paper-based sensor, folding method to increase sensitivity, and structural design model may provide a way for the research of flexible sensors. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based devices (μPADs) and wearable devices have been highly studied to be used as diagnostic tools due to their advantages such as simplicity and ability to provide instrument-free fast results. Diseases such as periodontitis and diabetes mellitus can potentially be detected through these devices by the detection of important biomarkers. This study describes the development of μPADs through craft cutter printing for glucose and nitrite salivary diagnostics. In addition, the use of μPADs integrated into a mouthguard as a wearable sensor for glucose monitoring is also presented. μPADs were designed to contain two detection zones for glucose and nitrite assays and a sampling zone interconnected by microfluidic channels. Initially, the analytical performance of the proposed μPADs was investigated and it provided linear behavior (r2 ≥ 0.994) in the concentration ranges between 0 to 2.0 mmol L−1 and 0 to 400 μmol L−1 for glucose and nitrite, respectively. Under the optimized conditions, the limits of detection achieved for glucose and nitrite were 27 μmol L−1 and 7 μmol L−1, respectively. Human saliva samples were collected from healthy individuals and patients previously diagnosed with periodontitis or diabetes and then analyzed on the proposed μPADs. The results found using μPADs revealed higher glucose concentration values in saliva collected from patients diagnosed with diabetes mellitus and greater nitrite concentrations in saliva collected from patients diagnosed with periodontitis, as expected. The results obtained on μPADs did not differ statistically from those measured by spectrophotometry. With the aim of developing paper-based wearable sensors, μPADs were integrated, for the first time, into a silicone mouthguard using a 3D-printed holder. The proof of concept was successfully demonstrated through the monitoring of the glucose concentration in saliva after the ingestion of chocolate. According to the results reported herein, paper-based microfluidic devices offer great potential for salivary diagnostics, making their integration into a silicone mouthguard possible, generating simple, low-cost, instrument-free, and powerful wearable sensors. [ABSTRACT FROM AUTHOR]

The results of a voltage-current characteristics study of sensors made of graphite paper with multiwall nanotubes are presented. It has been found experimentally that these samples are usable as gas sensors for the detection of NH, H, and Cl in air. [ABSTRACT FROM AUTHOR]

Paper has been present in the world of analytical chemistry for centuries, but it seems that just a few years back it was rediscovered as a valuable substrate for sensors. We can easily list some of the countless advantages of this simple cellulosic substrate, including mechanical properties, three-dimensional fibrous structure, biocompatibility and biodegradability, easiness of production and modification, reasonable price, and availability all over the world. Those characteristics make paper a first-choice substrate for disposable sensors and integrated sensing platforms. Nowadays, numerous examples of paper-based sensors are being presented in the literature. This review describes some of the most prominent examples classifying them by type of detection: optical (colorimetric, fluorescence, surface-enhanced Raman spectroscopy, and transmittance methods) and electrochemical (voltammetric, potentiometric, and conductivity-based methods). We take a closer look at recent advances in immunoassays fabricated on paper, excluding simple lateral flow tests assembled on nitrocellulose. This review also summarizes the main advantages and disadvantages of the use of paper as a substrate for sensors, as well as its impact on their performance and application, presents a short history of paper in analytical chemistry, and discusses fabrication methods and available sources of paper. [ABSTRACT FROM AUTHOR]

We present a deep learning-based framework to design and quantify point-of-care sensors. As a use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, commonly used for assessing risk of cardio-vascular disease (CVD). A machine learning-based framework was developed to (1) determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a sensing membrane, and (2) to accurately infer target analyte concentration. Using a custom-designed handheld VFA reader, a clinical study with 85 human samples showed a competitive coefficient-of-variation of 11.2% and linearity of R2 = 0.95 among blindly-tested VFAs in the hsCRP range (i.e., 0–10 mg/L). We also demonstrated a mitigation of the hook-effect due to the multiplexed immunoreactions on the sensing membrane. This paper-based computational VFA could expand access to CVD testing, and the presented framework can be broadly used to design cost-effective and mobile point-of-care sensors. [ABSTRACT FROM AUTHOR]

Graphene-nanosheet-based highly porous magnetite nanocomposites (GN-HPMNs) have been prepared using a simple solvothermal method and used as an immobilization matrix for the fabrication of a solid-state electrochemiluminescence (ECL) sensor on paper-based chips. Highly porous FeO nanocrystal clusters were coated with acrylate and wrapped tightly on the skeleton of graphene nanosheets. The structures and sizes of the GN-HPMNs could be tuned by varying the proportions of the solvents ethylene glycol and diethylene glycol. Then, the relatively highly porous ones with an average diameter of about 65 nm were combined with Nafion to form composite films on an electrode surface for immobilization of Ru(bpy) (bpy is 2,2′-bipyridine). Because of their porosity, negatively charged surface, and cooperative characteristics of magnetic nanomaterials and graphene, under an external magnetic field, the GN-HPMNs ensured effective immobilization, excellent electron transfer, and long-term stability of Ru(bpy) in the composite film. The sensor developed exhibited excellent reproducibility with a relative standard deviation of 0.65 % for 30 continuous cycles. It was found to be much more favorable for detecting compounds containing tertiary amino groups and DNAs with guanine and adenine. A detection limit (signal-to-noise ratio of 3) of 5.0 nM was obtained for tripropylamine. As an application example, 0.5 nM single-nucleotide mismatch could be detected. This was the first attempt to introduce magnetic nanomaterials and an external magnetic field into paper-based chips. The sensor developed has the advantages of high sensitivity, good stability, and wide potential applicability as well as simplicity, low cost, and good disposability. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

To deal with the highly nonlinear and time-varying characteristics of Batch Process, a model named adaptive stacking approximate kernel based broad learning system is proposed in this paper. This model innovatively introduces the approximate kernel based broad learning system (AKBLS) algorithm and the Adaptive Stacking framework, giving it strong nonlinear fitting ability, excellent generalization ability, and adaptive ability. The Broad Learning System (BLS) is known for its shorter training time for effective nonlinear processing, but the uncertainty brought by its double random mapping results in poor resistance to noisy data and unpredictable impact on performance. To address this issue, this paper proposes an AKBLS algorithm that reduces uncertainty, eliminates redundant features, and improves prediction accuracy by projecting feature nodes into the kernel space. It also significantly reduces the computation time of the kernel matrix by searching for approximate kernels to enhance its ability in industrial online applications. Extensive comparative experiments on various public datasets of different sizes validate this. The Adaptive Stacking framework utilizes the Stacking ensemble learning method, which integrates predictions from multiple AKBLS models using a meta-learner to improve generalization. Additionally, by employing the moving window method—where a fixed-length window slides through the database over time—the model gains adaptive ability, allowing it to better respond to gradual changes in industrial Batch Process. Experiments on a substantial dataset of penicillin simulations demonstrate that the proposed model significantly improves predictive accuracy compared to other common algorithms. [ABSTRACT FROM AUTHOR]

Urea is an important molecule in physiology and industry. The detection of urea by electrochemical sensors has broad application prospects in food detection and kidney disease screening. This paper introduces the preparation method of MWCNT/PANi electronic ink and its composite electrode, which was applied to the detection of urea. The linear range of the MWCNT/PANi composite electrode was increased by adjusting the concentration of MWCNT. The maximum linear range was 10 to 100 μM when 5.6 mg MWCNT was added. The sensitivity of the electrode was 2.435 × 10−4 mA mM−1 cm−2 and the detection limit was 53.9 nM. At the same time, an algorithm was proposed to identify and analyze the data collected by the electrochemical workstation in view of the errors existing in the calculation of the electrochemical sensor when analyzing the optimal response voltage of the electrode. This method was used to set constraints and screen the optimal working voltage. Experimental results show that this method has certain advantages. [ABSTRACT FROM AUTHOR]

Fruit recognition and location are the premises of robot automatic picking. YOLOv3 has been used to detect different fruits in complex environment. However, for the object with definite features, the complex network structure will increase the computing time and may cause overfitting. Therefore, this paper has carried out a lightweight design for the YOLOv3. This paper proposed an improved T-Net to detect tomato images. Firstly, the T-Net reduces the residual network layers. This paper changed the number of cycles in each group of the residual unit to 1, 2, 2, 1, and 1. Second, two feature layers with different scales are selected according to the features of tomatoes. Meanwhile, the convolutional layer at the neck has been reduced by two layers. Finally, the location and approximate diameter of the ripe tomato are obtained by combining the node information of the Intel D435i camera and T-Net in the Robot Operation System. T-Net obtains mean average precision (mAP) of 99.2%, F1-score of 98.9%, precision of 99.0%, and recall of 98.8% at a detection rate of 104.2 FPS. The proposed T-Net has outperformed the YOLOv3 with 0.4%, 0.1%, and 0.2% increase in precision, mAP, and F1-score. The detection speed of T-Net is 1.8 times faster than YOLOv3. The mean errors of the center coordinates and diameter of the tomato are 8.5 mm and 2.5 mm, respectively. This model provides a method for efficient real-time detection and location of tomatoes. [ABSTRACT FROM AUTHOR]

Digital technologies in, on, and around bicycles and cyclists are gaining ground. Collectively called Smart Cycling Technologies (SCTs), it is important to evaluate their impact on subjective cycling experiences. Future evaluations can inform the design of SCTs, which in turn can help to realize the abundant benefits of cycling. Wearable body sensors and advanced driver assistance systems are increasingly studied in other domains, however evaluation methods integrating such sensors and systems in the field of cycling research were under-reviewed and under-conceptualized. This paper therefore presents a systematic literature review and conceptual framework to support the use of body sensors in evaluations of the impact of SCTs on perceptions, emotions, feelings, affect, and more, during outdoor bicycle rides. The literature review (n = 40) showed that there is scarce research on this specific use of body sensors. Moreover, existing research designs are typically not tailored to determine impact of SCTs on cycling experience at large scale. Most studies had small sample sizes and explored limited sensors in chest belts and wristbands for evaluating stress response. The evaluation framework helps to overcome these limitations, by synthesizing crucial factors and methods for future evaluations in four categories: (1) experiences with SCTs, (2) experience measurements, (3) causal analysis, (4) confounding variables. The framework also identifies which types of sensors fit well to which types of experiences and SCTs. The seven directions for future research include, for example, experiences of psychological flow, sensors in e-textiles, and cycling with biofeedback. Future interactions between cyclists and SCTs will likely resemble a collaboration between humans and artificial intelligence. Altogether, this paper helps to understand if future support systems for cyclists truly make cycling safer and more attractive. [ABSTRACT FROM AUTHOR]

Locating tables in document images is the first step to extracting table information, and high location precision is required. The dominant approach of table detection is based on an object detection algorithm, and the detector defines the prediction task as a regression problem, which inevitably leads to positioning errors. To address this issue, this paper presents an approach called Border Line Correction (BLC) to refine the rough prediction results of the original detector through the table boundary lines extracted from the document image. Our approach transforms the regression task into a classification problem, thus avoiding the inherent regression error of the object detection algorithm. Traditional annotation methods are inadequate for table detection tasks as they fail to capture the completeness and purity of the detection results. Therefore, this study treats the correct position of a table as a tolerance region. Additionally, to overcome the limitations of existing datasets in the materials domain, we collected 1183 samples from scientific literature in the materials field and created the MatTab dataset, annotating the tables with tolerance regions. This paper use Cascade RCNN with Swin Transformer as baseline models, and BLC is utilized to optimize the detection results. Experimental results demonstrate significant improvements with BLC at an IOU of 0.95 on the MatTab, ICDAR2019, and ICDAR2017 datasets. In MatTab, the percentage of correctly detected complete and pure tables increased from 72.3% to 82.1%. [ABSTRACT FROM AUTHOR]

Chemical and biomolecular analysis form an integral part of food contamination testing and healthcare diagnostics. The majority of the currently established detection methods are expensive, sophisticated and time consuming. Microfluidic-based lab-on-a-chip systems offer a promising alternative in this regard by enabling the development of simpler and relatively cost-effective platforms for analytical applications. In this work, we report the fabrication of a portable sensor platform using selective wax impregnation of cellulosic paper. The application of the fabricated paper-based lab-on-a-chip device is demonstrated for the qualitative and quantitative detection of starch contamination in milk using colorimetry. Integration of a detection procedure with smartphone imaging allows for on-the-spot data collection, thus overcoming the challenges of sample storage, handling and transportation to lab facilities. This approach offers a versatile method for diagnostic and sensing applications in resource-limited areas, especially in developing and underdeveloped countries. [ABSTRACT FROM AUTHOR]

The bird's eye view, multi-scale and dense classes in remote sensing images challenge the object detection of remote sensing images. It is not satisfactory to directly apply the object detection method designed for natural scene images to the object detection of remote sensing images. In this paper, we propose a detector with enhanced feature extraction ability to solve the above challenges, namely TWDFPN. TWDFPN has designed the structure of a two-way feature pyramid network (TWFPN) by combining feature maps with different generation directions and different spatial resolutions, which not only improves the utilization of the underlying feature information, but also strengthens the repeated utilization of the feature information of the backbone network, and ultimately improves the feature extraction ability of the network. Meanwhile, the dense-connected module is used in TWFPN to enhance the feature representation ability through limited additional computation cost, which extends the network and deepens the network. To evaluate the effectiveness of the proposed algorithm, this paper carried out experiments on NWPUVHR-10 and RSOD public remote sensing datasets, and the average accuracy (mAP) of 92.98% and 96.16%, respectively, which achieves advanced performance. [ABSTRACT FROM AUTHOR]

A general and adaptable method is proposed to reliably extract quantitative information from smartphone images of microfluidic sensors. By analyzing and processing the color information of selected standard substances, the influence of light conditions, device differences, and human factors could be significantly reduced. Machine learning and multivariate fitting methods were proved to be effective for chroma correction, and a key element was the training of sample size and the fitting form, respectively. A custom APP was developed and validated using a high-sensitivity chromium ion quantification paper chip. The average chroma deviations under different conditions were reduced by more than 75% in RGB color space, and the concentration test error was reduced by more than half compared with the commonly used method. The proposed approach could be a beneficial supplement to existing and potential colorimetry-based detection methods. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the dynamic collaborative charging scheduling problem with multiple Mobile Chargers (MCs) in the Wireless Rechargeable Sensor Networks (WRSNs) with obstacles. Firstly, we use the Fresnel Diffraction Model (FDM) to describe the influences of obstacles on the charging process. Secondly, we propose a new charging group division algorithm, which can select the sets of the sensor nodes that can be charged simultaneously and determine the selection ranges of the charging spots. Thirdly, we propose a charging spots selection algorithm based on the FDM, which can not only reduce the number of sensor failures, but also guarantee the high-level charging utility. Fourthly, we propose a dynamic zonal collaborative charging scheduling scheme. It divides the charging zones to achieve balanced distribution of the charging loads. When a zone is not schedulable, our scheme will redistribute its high energy-cost charging tasks to the zone with less load for dynamic adjustment. When some charging tasks can not be adjusted, our scheme will discard the ones with less contribution, minimizing the losses of the network. Finally, we conduct a large number of simulations to verify the performances of our work. The simulation results show that our scheme has obviously better performances compared with the other arts. [ABSTRACT FROM AUTHOR]

In this paper, the hazardous effects of various volatile organic compounds (VOCs) on human health and its detection methods have been discussed. This paper also presents the comprehensive study on various VOC sensing mechanisms, their advantages, limitations and key design challenges. It is also reported that acoustic wave sensors are more suitable for VOC detection because of its various advantages as compared to other techniques such as, extended lifetime, miniaturization, high sensitivity, less/zero secondary pollution, low power consumption, lower detection limit and low-cost. The main focus of this paper is on bulk acoustic wave (BAW) devices for VOC detection. The development of film bulk acoustic wave resonators (FBARs) in various aspects such as, structural classification, operating modes, selection criteria of materials (electrode, piezoelectric and sensing layer) and their deposition methods, fabrication process flow and applications have been discussed in this detailed review. Various performance enhancement methods for efficient VOC detection using FBARs have been described in detail to design, model, analyze and optimize a FBAR sensors for the detection of various hazardous VOCs with enriched sensitivity and selectivity. [ABSTRACT FROM AUTHOR]

Kenaf is a renewable resource that has recently attracted great interest owing to its fast growth which makes a large volume of raw material in a short time. Due to its large surface area and high water adsorption, kenaf cellulose was readily prepared as a substrate immobilized with dimethylglyoxime (DMG) for specific Ni(II) sensor. Herein, a simple and highly sensitive colorimetric detection of Ni(II) using kenaf cellulose-based 3D printed device was successfully created. The large volume (1.0 mL) of Ni(II) solution can be applied on a kenaf cellulose-based 3D printed sensor coupled with a multilayer of adsorption pads. This system provides a wide linear range (0.1–100 mg/L) with an extremely low limit of detection (40 μg/L), significantly improved from a conventional filter paper-based device (1.0 mg/L) for 25 uses, and it enables highly sensitive colorimetric detection of Ni(II) contamination in industrial wastewater without any use of external instruments validated with a standard ICP-OES method. [ABSTRACT FROM AUTHOR]

Temporal action detection is an important and fundamental video understanding task that aims to locate the temporal regions where human actions or events may occur and to identify the classes of actions in untrimmed videos. The main challenge of temporal action detection is that videos are usually of different durations and untrimmed. Although existing methods have achieved better results in recent years, there are still some challenges, such as a lack of full utilisation of video context features, insufficient accuracy of generated action boundaries and failure to consider the relationship between proposals. To address the above issues, this paper proposes a Multi-branch Boundary Generation Network (MBGNet) with temporal context aggregation. It improves the performance of temporal action proposal generation by exploiting rich temporal context features and complementary boundary generators.First, we propose a multi-path temporal context feature aggregation (MTCA) module to exploit "local and global" contextual temporal features for the generation of temporal action proposals. Second, in order to generate accurate action boundaries, we design a multi-branch temporal boundary detector (MBG) to optimise the prediction results by exploiting the complementary relationship between the two boundary detectors.In addition, to accurately predict the confidence of densely distributed proposals, we design a proposal relation-aware module (PRAM) that exploits global correlation for proposal relationship modelling. Experiments on the popular datasets ActivityNet1.3, THUMOS14, and HACS demonstrate the effectiveness of the method proposed in this paper on the task of temporal action proposal generation, which can generate action proposals with high precision and recall. Moreover, combining with existing action classifiers can also achieve better performance in temporal action detection.These results demonstrate the effectiveness of the method in this paper in improving the accuracy of temporal action proposal generation and detection. [ABSTRACT FROM AUTHOR]

Lead-free Cs3Bi2I9 single crystals have been demonstrated to be promising materials for direct X-ray detectors with remarkable performance. However, their application for 2D X-ray imaging is hindered by their time-consuming preparation and limited crystal size. In this paper, a thick Cs3Bi2I9 perovskite film fabricated via facile spray coating at a low processing temperature, which increases the area of the photoactive film, reduces the processing time, decreases the energy budget and the production cost, and enhances the production yield due to high material utilization, has great potential for commercial applications. Careful control of the processing temperature and intervals during spray coating results in a dense and thick perovskite film with well-stacked perovskite domains. The compact perovskite film enhances the charge transport capability of the Cs3Bi2I9 perovskite film and reduces the dark current density of the X-ray detector. The resultant X-ray detector, prepared through a two-step spray coating process, exhibited a sensitivity of 127.23 μC Gyair−1 cm−2 and a detection limit of 7.4 μGyair s−1. In addition, the device delivers long-term stability with a consistent photoresponse when exposed to consecutive X-ray pulse irradiation. A two-step spray coating process (first step: 20 cycles of spray coating at 110 °C with 40 s intervals; second step: 180 cycles of spray coating at 130 °C with 20 s intervals) was employed to a produce large-area, compact, and thick Cs3Bi2I9 perovskite film for the application of direct X-ray detectors. The fabricated device achieved a large active area of 150 mm2, a sensitivity of 127.23 μC Gyair−1 cm−2, a detection limit of 7.4 μGyairs−1, and durability after long-term X-ray pulse irradiation. [ABSTRACT FROM AUTHOR]

The object scale of a small object scene changes greatly, and the object is easily disturbed by a complex background. Generic object detectors do not perform well on small object detection tasks. In this paper, we focus on small object detection based on FocusDet. FocusDet refers to the small object detector proposed in this paper. It consists of three parts: backbone, feature fusion structure, and detection head. STCF-EANet was used as the backbone for feature extraction, the Bottom Focus-PAN for feature fusion, and the detection head for object localization and recognition.To maintain sufficient global context information and extract multi-scale features, the STCF-EANet network backbone is used as the feature extraction network.PAN is a feature fusion module used in general object detectors. It is used to perform feature fusion on the extracted feature maps to supplement feature information.In the feature fusion network, FocusDet uses Bottom Focus-PAN to capture a wider range of locations and lower-level feature information of small objects.SIOU-SoftNMS is the proposed algorithm for removing redundant prediction boxes in the post-processing stage. SIOU multi-dimension accurately locates the prediction box, and SoftNMS uses the Gaussian algorithm to remove redundant prediction boxes. FocusDet uses SIOU-SoftNMS to address the missed detection problem common in dense tiny objects.The VisDrone2021-DET and CCTSDB2021 object detection datasets are used as benchmarks, and tests are carried out on VisDrone2021-det-test-dev and CCTSDB-val datasets. Experimental results show that FocusDet improves mAP@.5% from 33.6% to 46.7% on the VisDrone dataset. mAP@.5% on the CCTSDB2021 dataset is improved from 81.6% to 87.8%. It is shown that the model has good performance for small object detection, and the research is innovative. [ABSTRACT FROM AUTHOR]

Due to its excellent performance, polyurethane rubber is often used as a protective baffle for the automatic passing neutral section sensor to protect the sensor from the impact of silt and stones, while the high-speed train is operating. However, this paper conducts case analysis of abnormal cracking of the sensor baffle used in high-speed trains. In fact, this kind of problem has happened several times, and the previous solution method was to replace the failed baffle. But such kind of method mainly treats the faults instead the root cause analysis, so it still brings the safety issue to the normal operation of the train. In order to avoid such potential accidents, this paper adopted a variety of material analysis methods to detect the properties of materials and analyzed the damaged area in detail by combining macroscopic and microscopic methods. Finally, the root cause of the failure was further confirmed by confirmatory experiment. Based on the above comprehensive analysis, a reasonable solution is proposed to avoid the recurrence of the accidents and provide an important basis for solving the complicated polyurethane rubber failure problem quickly, correctly and effectively. [ABSTRACT FROM AUTHOR]

The increasing population of diabetic patients, especially in developing countries, has posed a serious risk to the health sector, so that the lack of timely diagnosis and treatment process of diabetes can lead to threatening complications for the human lifestyle. Here, a multiple sensor was fabricated on a paper substrate for rapid detection and controlling the progress of the diabetes disease. The proposed sensor utilized the sensing ability of porphyrazines, pH-sensitive dyes and silver nanoparticles in order to detect the differences in saliva composition of diabetic and non-diabetic patients. A unique color map (sensor response) was obtained for each studied group, which can be monitored by a scanner. Moreover, a good correlation was observed between the colorimetric response resulting from the analysis of salivary composition and the fasting blood glucose (FBG) value measured by standard laboratory instruments. It was also possible to classify participants into two groups, including patients caused by diabetes and those were non-diabetic persons with a total accuracy of 88.9%. Statistical evaluations show that the multiple sensor can be employed as an effective and non-invasive device for continuous monitoring of diabetes, substantially in the elderly. [ABSTRACT FROM AUTHOR]

In the era of Machine Learning and Internet of Things, numerous sensing devices collect sensory information for various industries. Aerobics, a popular and energetic sport, benefits from analyzing and researching the training approaches of high-level university and college teams. However, the complexity and diversity of sensor data require systematic classification and adaptive preprocessing for effective human motion recognition. Based on these, this paper aimed at the balance ability of aerobics students and analyzed and studied the balance ability of aerobics students through wireless sensor networks. Furthermore, this paper provides a comprehensive overview of how to use a class of enhanced machine learning approaches, such as Artificial Neural Networks, to facilitate analytics and learning in the wireless sensor networks domain by utilizing continuous sensor data. During experimental work, this study selects 10 third-year students in a college of physical education, and the subjects were divided into two groups: the experimental group and the control group collecting their data by continuous sensors. The experimental group mainly carried out special training and elastic band exercises within eight weeks. After eight weeks of elastic training, eight indexes of the left and right legs, including anterior (ANT), anterolateral (ALAT), lateral (LAT), posterolateral (PLAT), posterior (POST), posteromedial (PMED), medial (MED), and anteromedial (AMED) were improved (P < 0.01), but the indexes with the left foot as the fulcrum were not significantly improved. By using the 8-week elastic band training method, the technical indexes such as single leg lifting and single leg rotation of aerobics students were significantly improved (P < 0.05). [ABSTRACT FROM AUTHOR]

Handwritten music recognition (HMR) is the technology of transcribing the content of images of music scores. The accurate detection of music objects at the page level is one of the main challenges of HMR. Thus far, the existing methods suffer from the tiny and dense nature of handwritten music notations and realize positive detection accuracy only on snippets. In this paper, we propose a detector that consists of a staff line removal model and a handwritten music object detection model for page-level handwritten music object recognition. First, an end-to-end staff line removal model R_Staff_Net based on residual learning reduces the complexity of page-level detection. Second, we developed an improved YOLO-V4 model for handwritten music object detection. The improvements mainly concern the adoption of a de-coupled detection head and visual attention module in the YOLO-V4, and an adaptive multi-scale feature fusion module AMFFM is used to enhance the textures and features of tiny music symbols in the deep convolution layers, and the gradient harmonized mechanism is utilized to address the inherent imbalance between music objects. We verified the R_Staff_Net and the improved YOLO-V4 model on the ICDAR/GREC staff line removal dataset and the MUSCIMA++ dataset, respectively. The experiments highlight that R_Staff_Net presents outstanding performance with an F-M score of 98.64%, and our improved YOLO-V4 model is superior to other handwritten music symbol detection methods with a mean average precision (mAP) of 91.8% when addressing page-level input. Although the experimental results of the detector show that the R_Staff_Net helps little to the overall mAP, the network is beneficial for symbols that are similar to staff lines or heavily overlap staff lines. [ABSTRACT FROM AUTHOR]

Two colorimetric nanostructured sensor phases (Color-NSPs) for the determination of low concentrations of acid vapors in the atmosphere of paper storage rooms have been designed and characterized. The acid vapor determination is based on the color change that occurs in polyaniline (PANI) in the presence of acids when it goes from its emeraldine base form (blue) to its emeraldine salt form (green). To synthesize the Color-NSPs, two methods have been used, a one-step method performed by grafting polyaniline onto a cellulose membrane (Cellu-PANI) and a two-step method in which in the first step, polyaniline is grafted onto the surface of polymeric nanoparticles (NPs-PANI), and in a second step, NPs-PANI are immobilized into the pores of a nylon membrane (Nylon-NPs PANI). The response of the sensors versus acid vapor was measured by color coordinates with a photographic camera. A linear response range from 1 ppmv to 7 ppmv was found for both sensors, and the detection limits were 0.95 ppmv (1.2 % RSD) and 0.40 ppmv (0.8 % RSD) for Cellu-PANI and Nylon-NPs PANI, respectively. In addition, both sensors showed complete reversibility and a short exposition time (5 min). The potential applicability of the Color-NSPs in the control of the exposure of paper heritage collections to outdoor- and indoor-generated gaseous pollutants was demonstrated by determining acid vapors in museums. The method was validated with an external reference method; the paired test was applied, and p-values greater than 5% were obtained, indicating an excellent correlation and showing that the Color-NSPs reported are simple, fast, and an economical alternative to control and protect cultural heritage materials in indoor environments. [ABSTRACT FROM AUTHOR]

The rapid proliferation of the Internet of Things (IoT) technologies, underwater sensors and tracking systems, and advances in signal processing and information systems aid in the development of sensing technologies as well as software-defined networks, which enable effective water monitoring and modeling. The paper by Jayagopal et al. ([5], [6]) "Weather-based maize yield forecast in Saudi Arabia using statistical analysis and machine learning" focused on forecasting maize yield production in different climate changes in Saudi Arabia. Acta Geophys. https://doi.org/10.1007/s11600-022-00849-w 9 Mathivanan SK, Jayagopal P. Utilizing satellite and UAV data for crop yield prediction and monitoring through deep learning. [Extracted from the article]

Abstract  Heavily impressed writing impressions in paper and impressions in glossy brochures are often difficult or impossible to develop using the electrostatic detection apparatus (ESDA, manufactured by Foster and Freeman). Oblique and specular reflectance lighting can be used but they are time-consuming techniques and not always very successful. This paper briefly describes a scanning and image-processing method for producing excellent images of such writing impressions. [ABSTRACT FROM AUTHOR]

The global stress distribution and state parameter analysis of the building's main structure is an urgent problem to be solved in the online state assessment technology of building structure health. In this paper, a stress curve clustering algorithm of fiber Bragg grating stress sensor based on density clustering algorithm is proposed. To solve the problem of large dimension and sparse sample space of sensor stress curve, the distance between samples is measured based on improved cosine similarity. Aiming at the problem of low efficiency and poor effect of traditional clustering algorithm, density clustering algorithm based on mutual nearest neighbor is used to cluster. Finally, the classification of the daily stress load characteristics of the sensor is realized, which provides a basis for constructing the mathematical analysis model of building health. The experimental results show that the stress curve clustering method proposed in this paper is better than the latest clustering algorithms such as HDBSCAN, CBKM, K-mean++,FINCH and NPIR, and is suitable for the feature classification of stress curves of fiber Bragg grating sensors. [ABSTRACT FROM AUTHOR]

The work reported in this paper describes the design and simulation of a novel current mirror readout circuit based MOSFET integrated cantilever structure for micro-gram (μ g) mass sensing applications. The proposed mass sensing structure consist of silicon cantilever as the micromechanical structure, MOSFET as strain sensing element and current mirror as a readout circuit. In this paper, studies have been carried out on three structures (a) n-channel MOSFET based resistive loaded current mirror integrated cantilever mass sensor (b) p-channel MOSFET based resistive loaded current mirror integrated cantilever mass sensor and (c) n- and p- channel MOSFETs based dual current mirror integrated cantilever mass sensor. The input MOSFET(s) of the current mirror acts as the reference transistor(s) while the output MOSFET(s) of the mirror circuit acts as strain sensing element(s) to measure the cantilever deflection under externally applied load. The structural and the electrical characteristics of the sensors were simulated using COMSOL Multiphysics and TSPICE software respectively. Simulation results showed a sensitivity of 21.89 μ V/ μ g, 1 μ V/ μ g and 115.57 μ V/ μ g for nMOS, pMOS and dual channel cantilever mass sensor. The fabrication steps and mask layout of the proposed sensor have also been presented in this paper. The novelty of this work is emphasized by the innovative integration of a CMOS current mirror circuit with a cantilever for mass sensing. [ABSTRACT FROM AUTHOR]

This paper proposes a detector that focuses on multi-scale detection problems and effectively enhances the detection performance to solve the problem that is hard to detect minor traffic signs. This detector, called YOLOF-F (you only look one-level feature fusion), is a single-stage detector that extracts multi-scale feature information from a single layer of fusion feature. First, we propose FFM (feature fusion module) to fuse different scales. Next, we offer a new encoder CDE (corner dilated encoder) to enhance the angular point information in the feature map, improve position regression accuracy, and maintain a faster detection speed. Finally, YOLOF-F achieved 74.57% and 77.23% of the AP on the GTSDB and CTSD datasets and reached 32 FPS. Extensive experiments validate that YOLOF-F is faster and more effective than most traffic sign detection methods. [ABSTRACT FROM AUTHOR]