Textile‐based electronic techniques that can in real‐time and noncontact detect the respiration rate and respiratory arrest are highly desired for human health monitoring. Yarn‐shaped humidity sensor ...is fabricated based on a sensitive fiber with relatively high specific surface area and abnormal cross‐section. The response and recovery time of the yarn‐shaped humidity sensor is only 3.5 and 4 s, respectively, with little hysteresis, because of the hydrophobic property of these functional fibers and the grooves on the surface of the fibers, which is much faster than those of the commercial polyimide materials. Moreover, a battery‐free LC wireless testing system combined with the yarn‐shaped sensor is fabricated, which is further successfully imbedded into the intelligent mask to detect human breath. Based on the detection of LC wireless testing system, the frequency of 50.25 MHz under the exhaled condition shifts to 50.86 MHz under the inhaled situation of humidity sensor. In essence, the functional yarns with proper structure, would be an excellent candidature to the yarn‐shaped humidity sensor, in which there are good performance and wide application possibilities, eventually offering a facile method for the wireless detection of human physiological signals in the field of electronic fabrics.
A yarn‐shaped humidity sensor is fabricated using sensitive fibers with a relatively high surface area and specific cross‐section. It exhibits an extraordinary humidity sensitivity performance with little hysteresis. A full‐textile wireless and battery‐free humidity sensitive system is then successfully developed for the detection of human physiological signals, i.e., the respiration rate and respiratory arrest.
Bombyx mori silk fibers exhibit significant potential for applications in smart textiles, such as fiber sensors, fiber actuators, optical fibers, and energy harvester. Silk fibroin (SF) from B. mori ...silkworm fibers can be reconstructed/functionalized at the mesoscopic scale during refolding from the solution state into fibers. This facilitates the mesoscopic functionalization by engaging functional seeds in the refolding of unfolded SF molecules. In particular, SF solutions can be self‐assembled into regenerated fiber devices by artificial spinning technologies, such as wet spinning, dry spinning, microfluidic spinning, electrospinning, and direct writing. Meso‐functionalization manipulates the SF property from the mesoscopic scale, transforming the original silk fibers into smart fiber devices with smart functionalities, such as sensors, actuators, optical fibers, luminous fibers, and energy harvesters. In this review, the progress of mesoscopic structural construction from SF materials to fiber electronics/photonics is comprehensively summarized, along with the spinning technologies and fiber structure characterization methods. The applications, prospects, and challenges of smart silk fibers in textile devices for wearable personalized healthcare, self‐propelled exoskeletons, optical and luminous fibers, and sustainable energy harvesters are also discussed.
This review summarizes the progress of mesoscopic structural construction from silk fibroin materials to fiber devices. The significance of meso‐functionalization of the mesoscopic hieratical structure of silk fibers in fiber electronics/photonics are highlighted. Applications, prospects, and challenges of functional silk fibers in textile devices for wearable personalized healthcare, self‐propelled exoskeletons, optical and luminous fibers, and sustainable energy harvesters are discussed.
Seismic imaging techniques play a crucial role in interpreting subsurface geological structures by analyzing the propagation and reflection of seismic waves. However, traditional methods face ...challenges in achieving high resolution due to theoretical constraints and computational costs. Leveraging recent advancements in deep learning, this study introduces a neural network framework that integrates Transformer and Convolutional Neural Network (CNN) architectures, enhanced through Adaptive Spatial Feature Fusion (ASFF), to achieve high-resolution seismic imaging. Our approach directly maps seismic data to reflection models, eliminating the need for post-processing low-resolution results. Through extensive numerical experiments, we demonstrate the outstanding ability of this method to accurately infer subsurface structures. Evaluation metrics including Root Mean Square Error (RMSE), Correlation Coefficient (CC), and Structural Similarity Index (SSIM) emphasize the model's capacity to faithfully reconstruct subsurface features. Furthermore, noise injection experiments showcase the reliability of this efficient seismic imaging method, further underscoring the potential of deep learning in seismic imaging.
With the continuous development of the Internet, big data have been reflected in various industries. In the field of finance, using big data can promote the stable development of the financial field ...and control the unstable factors within a reasonable range. This paper first understands the content of big data, then explains the challenges faced by the application of big data in the financial field, and finally understands the strategy of big data application in the financial field, which provides a reference for the research of relevant personnel.
Highlights
Full-fiber auxetic-interlaced yarn sensor was fabricated by a continuous and mass-producible computerized wrapping spinning technology.
Auxetic-interlaced yarn sensor shows a Poisson’s ...ratio of − 1.5, a robust mechanical property (0.6 cN/dtex), and a fast train-resistance responsiveness (0.025 s).
A novel sign-language translation glove was developed to recognize the full English alphabet and translate the wearer’s sign language to text.
Yarn sensors have shown promising application prospects in wearable electronics owing to their shape adaptability, good flexibility, and weavability. However, it is still a critical challenge to develop simultaneously structure stable, fast response, body conformal, mechanical robust yarn sensor using full microfibers in an industrial-scalable manner. Herein, a full-fiber auxetic-interlaced yarn sensor (AIYS) with negative Poisson’s ratio is designed and fabricated using a continuous, mass-producible, structure-programmable, and low-cost spinning technology. Based on the unique microfiber interlaced architecture, AIYS simultaneously achieves a Poisson’s ratio of−1.5, a robust mechanical property (0.6 cN/dtex), and a fast train-resistance responsiveness (0.025 s), which enhances conformality with the human body and quickly transduce human joint bending and/or stretching into electrical signals. Moreover, AIYS shows good flexibility, washability, weavability, and high repeatability. Furtherly, with the AIYS array, an ultrafast full-letter sign-language translation glove is developed using artificial neural network. The sign-language translation glove achieves an accuracy of 99.8% for all letters of the English alphabet within a short time of 0.25 s. Furthermore, owing to excellent full letter-recognition ability, real-time translation of daily dialogues and complex sentences is also demonstrated. The smart glove exhibits a remarkable potential in eliminating the communication barriers between signers and non-signers.
The ability to pattern natural polymers at different scales is extremely important for many research areas, such as cell culture, regenerative medicine, bioelectronics, tissue engineering, degradable ...implants, and photonics. For the first time, the use of wool keratin (WK) as a structural biomaterial for fabricating precise protein microarchitectures is presented. Through straightforward biochemical processes, modified WK proteins become intrinsically photoreactive without significant changes in protein structure or function. Under light irradiation, intermolecular chemical crosslinking between WK molecules can be successfully initiated by using commercially available photoinitiators. As a result, high‐performance WK patterning on the micrometer scale (µm) can be achieved through a combination of water‐based photolithography techniques. By simply mixing with nanoparticles, enzymes, and other dopants, various “functional WK resists” can be generated. In addition, without the addition of any cell‐adhesive ligands, these patterned protein microstructures are demonstrated as bio‐friendly cellular substrates for the spatial guidance of cells on their surface. Furthermore, periodic microfabricated WK structures in complex patterns that display typical iridescent behavior can be designed and formed over macroscale areas (cm).
“Photoresist‐like” wool keratin is used for fabricating well‐defined, high‐quality, and high‐resolution protein patterns over large areas via direct‐write photolithography without the need of toxic chemicals, high temperature, or harsh processing conditions. Moreover, the design and development of such large‐scale sustainable protein microstructures have widespread applications, such as the spatial guidance of cells and soft optical systems.
Turning insulating silk fibroin materials into conductive ones turns out to be the essential step toward achieving active silk flexible electronics. This work aims to acquire electrically conductive ...biocompatible fibers of regenerated Bombyx mori silk fibroin (SF) materials based on carbon nanotubes (CNTs) templated nucleation reconstruction of silk fibroin networks. The electronical conductivity of the reconstructed mesoscopic functional fibers can be tuned by the density of the incorporated CNTs. It follows that the hybrid fibers experience an abrupt increase in conductivity when exceeding the percolation threshold of CNTs >35 wt%, which leads to the highest conductivity of 638.9 S m−1 among organic‐carbon‐based hybrid fibers, and 8 times higher than the best available materials of the similar types. In addition, the silk‐CNT mesoscopic hybrid materials achieve some new functionalities, i.e., humidity‐responsive conductivity, which is attributed to the coupling of the humidity inducing cyclic contraction of SFs and the conductivity of CNTs. The silk‐CNT materials, as a type of biocompatible electronic functional fibrous material for pressure and electric response humidity sensing, are further fabricated into a smart facial mask to implement respiration condition monitoring for remote diagnosis and medication.
A new conceptual silk meso‐fibrous material for biocompatible electronic applications is developed by carbon nanotubes meso reconstruction. It can be adopted to fabricate various fibrous sensors, i.e., electronic humidity sensors. In combination with internet of things (IoTs) and artificial intelligence technologies, a remote respiratory condition monitoring and diagnosis can be achieved.
As a key sensor, radar plays an important role in obtaining war information. However, radar will be affected by the deteriorating electromagnetic environment on the battlefield. Therefore, it is ...necessary to carry out electromagnetic interference effect experiments to improve the anti-interference ability. In the electromagnetic interference experiment of the radar TR component, the signal output from the signal generator passes through the power amplifier, the transmitting antenna and the receiving antenna, and then is applied to the TR component. In the experiment, to obtain a certain size of interference signal acting on the TR component, it is often necessary to manually adjust the output signal strength of the signal generator repeatedly through human experience. The experimental process is complicated, and the experimental error is large. Therefore, it will make the experiment more convenient and accurate to achieve the desired interference signal size on the TR component by accurately predicting the output signal strength value of the signal generator, which has important practical significance. This paper proposes an IGWO-SVR based signal generator output signal strength prediction model, which includes Improved Grey Wolf Optimizer (IGWO) and Support Vector Regression (SVR) algorithms. IGWO is a new swarm optimization algorithm proposed in this paper. By improving the convergence factor a and the final position of <inline-formula> <tex-math notation="LaTeX">\omega </tex-math></inline-formula> wolf, IGWO solves the problems that the traditional GWO algorithm easily falls into local optimum and the convergence speed is slow. IGWO is used to optimize two hyperparameters of SVR (penalty coefficient C and kernel parameter <inline-formula> <tex-math notation="LaTeX">\gamma </tex-math></inline-formula>). SVR is used to predict the output signal strength value of the signal generator. To prove the validity of the IGWO-SVR, comparison experiments are made between the IGWO-SVR and 20 other models. The real data obtained from the experiments of electromagnetic interference effect by irradiation method are selected as the experimental data. Mean Absolute Error (MAE), Mean Squared Error (MSE), and Fitting Degree R Squared <inline-formula> <tex-math notation="LaTeX">(R^{2}) </tex-math></inline-formula> are used to evaluate the overall performance of the models. Through comparative experiments, the MAE of the IGWO-SVR model is 1.1481, MSE is 2.6679, <inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula> is 0.9430, and its performance in various evaluation indexes is better than other models.
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•A novel sensor array was constructed based on two oxidants doped carbon dots.•The array discriminated and quantified amyloidogenic proteins with high accuracy.•The array could ...monitor the fibrillation process of amyloidogenic proteins.•The array could discriminate cancer patients and healthy people based on serum.•It provided a discriminative and adaptive tool for biomedical research and disease diagnosis.
Disease-related proteins are biomarkers for disease diagnosis and treatment, e.g. amyloidogenic proteins are highly related to neurodegenerative diseases. Detection and monitoring of disease-related proteins is significant for the biomedical research and early disease diagnosis. Thus, the rapid, sensitive and low-cost approach is highly desired. For this purpose, herein, a novel dual-element sensor array was constructed based on two carbon nanodots (CDs). The CDs were prepared using citric acid and Congo red as carbon source by deliberately doping with two different oxidants, i.e. ammonium persulfate and hydrogen peroxide. The fluorescence of CDs could be efficiently and selectively quenched by proteins to different degrees, in which electrostatic and hydrophobic interactions might co-contribute to the sensing process. In the differentiability test, the established sensor array could accurately discriminate four amyloidogenic proteins and two serum proteins with a classification accuracy of 100%. The applicability could be extended to proteins quantification and mixture proteins discrimination. Typically, fibrillation stages of amyloidogenic protein (α-synuclein as example) in artificial cerebrospinal fluid were differentiated by the array, which was meaningful to illustrate the process of neurodegenerative diseases. Moreover, the sensor array successfully distinguished cancer patients (liver and breast cancers) and healthy people based on μL-level of serum sample, demonstrating its potential for fast screen of cancer at large scale. The constructed sensor array based on the oxidants doped CDs was promising for disease-related proteins discrimination, quantification and progressive fibrillation monitoring, and afforded a powerful discriminative and adaptive tool for biomedical research and disease diagnosis.
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