In this paper, we propose a novel method for temperature measurement using surface acoustic wave (SAW) temperature sensors on curved or irregular surfaces. We integrate SAW resonators onto flexible printed circuit boards (FPCBs) to ensure better conformity of the temperature sensor with the surface of the object under test. Compared to traditional rigid PCBs, FPCBs offer greater dynamic flexibility, lighter weight, and thinner thickness, which make them an ideal choice for making SAW devices working for temperature measurements under curved surfaces. We design a temperature sensor array consisting of three devices with different operating frequencies to measure the temperature at multiple points on the surface of the object. To distinguish between different target points in the sensor array, each sensor operates at a different frequency, and the operating frequency bands do not overlap. This differentiation is achieved using Frequency Division Multiple Access (FDMA) technology. Experimental results indicate that the frequency temperature coefficients of these sensors are -30.248 ppm/°C, -30.195 ppm/°C, and -30.115 ppm/°C, respectively. In addition, the sensor array enables wireless communication via antenna and transceiver circuits. This innovation heralds enhanced adaptability and applicability for SAW temperature sensor applications.
Ultrasonic wireless power transfer technology (UWPT) represents a key technology employed for energizing implantable medical devices (IMDs). In recent years, aluminum nitride (AlN) has gained significant attention due to its biocompatibility and compatibility with complementary metal-oxide-semiconductor (CMOS) technology. In the meantime, the integration of scandium-doped aluminum nitride (Al90.4%Sc9.6%N) is an effective solution to address the sensitivity limitations of AlN material for both receiving and transmission capabilities. This study focuses on developing a miniaturized UWPT receiver device based on AlScN piezoelectric micro-electromechanical transducers (PMUTs). The proposed receiver features a PMUT array of 2.8 × 2.8 mm2 comprising 13 × 13 square elements. An acoustic matching gel is applied to address acoustic impedance mismatch when operating in liquid environments. Experimental evaluations in deionized water demonstrated that the power transfer efficiency (PTE) is up to 2.33%. The back-end signal processing circuitry includes voltage-doubling rectification, energy storage, and voltage regulation conversion sections, which effectively transform the generated AC signal into a stable 3.3 V DC voltage output and successfully light a commercial LED. This research extends the scope of wireless charging applications and paves the way for further device miniaturization by integrating all system components into a single chip in future implementations.
The surface acoustic wave (SAW) temperature sensor has received significant attention due to its wirelessly powered, battery-free, and chipless capabilities. This paper proposes a wireless sensing system comprising a one-port SAW resonator, helix antenna, and transceiver circuit. The SAW resonator used in this system is based on aluminum nitride (AlN) thin film, which exhibits high velocity and excellent piezoelectric properties. Simulations and experiments were conducted to investigate the performance of the designed SAW resonator. A helix antenna was also designed using finite element simulation to facilitate signal transmission between the SAW temperature sensor and the transceiver. An impedance-matching network was introduced between the helix antenna and the SAW resonator to optimize signal transmission. When the wireless SAW temperature sensor was placed within a certain distance of the mother antenna, the reflection peak of the SAW resonator was observed in the spectrum of the return signal. The frequency of the echo signal increased almost linearly as the temperature increased during the temperature tests. The fitted temperature coefficient of frequency (TCF) was -31.34 ppm/°C, indicating that the wireless temperature sensing system has high-temperature sensitivity.
Ultrasonic nondestructive testing (NDT) usually utilizes conventional bulk piezoelectric transducers as transceivers. However, the complicated preparation and assembly process of bulk piezoelectric ceramics limits the development of NDT probes toward miniaturization and high frequency. In this paper, a 4.4 mm × 4.4 mm aluminum nitride (AlN) piezoelectric micromachined ultrasonic transducer (PMUT) array is designed, fabricated, characterized, and packaged for ultrasonic pulse-echo NDT of solids for the first time. The PMUT array is prepared based on the cavity silicon-on-insulator (CSOI) process and packaged using polyurethane (PU) material with acoustic properties similar to water. The fabricated PMUT array resonates at 2.183 MHz in air and at around 1.25 MHz after PU encapsulation. The bandwidth of the packaged PMUT receiver (244 kHz) is wider than that of a bulk piezoelectric transducer (179 kHz), which is good for axis resolution improvement. In this work, a hybrid ultrasonic NDT probe is designed using two packaged PMUT receivers and one 1.25 MHz bulk transmitter. The bulk transmitter radiates an ultrasonic wave into the sample, and the defect echo is received by two PMUT receivers. The 2D position of the defect could be figured out by time-of-flight (TOF) difference, and a 30 mm × 65 mm detection area is acquired. This work demonstrates the feasibility of applying AlN PMUTs to ultrasonic NDT of solids and paves the way toward a miniaturized NDT probe using AlN PMUT technology.
Geographical background and dispersal ability may strongly influence assemblage dissimilarity; however, these aspects have generally been overlooked in previous large-scale beta diversity studies. Here, we examined whether the patterns and drivers of taxonomic beta diversity (TBD) and phylogenetic beta diversity (PBD) of breeding birds in China vary across (1) regions on both sides of the Hu Line, which demarcates China's topographical, climatic, economic, and social patterns, and (2) species with different dispersal ability. TBD and PBD were calculated and partitioned into turnover and nestedness components using a moving window approach. Variables representing climate, habitat heterogeneity, and habitat quality were employed to evaluate the effects of environmental filtering. Spatial distance was considered to assess the impact of dispersal limitation. Variance partitioning analysis was applied to assess the relative roles of these variables. In general, the values of TBD and PBD were high in mountainous areas and were largely determined by environmental filtering. However, different dominant environmental filters on either side of the Hu Line led to divergent beta diversity patterns. Specifically, climate-driven species turnover and habitat heterogeneity-related species nestedness dominated the regions east and west of the line, respectively. Additionally, bird species with stronger dispersal ability were more susceptible to environmental filtering, resulting in more homogeneous assemblages. Our results indicated that regions with distinctive geographical backgrounds may present different ecological factors that lead to divergent assemblage dissimilarity patterns, and dispersal ability determines the response of assemblages to these ecological factors. Identifying a single universal explanation for the observed pattern without considering these aspects may lead to simplistic or incomplete conclusions. Consequently, a comprehensive understanding of large-scale beta diversity patterns and effective planning of conservation strategies necessitate the consideration of both geographical background and species dispersal ability.
Biodiversity , Ecosystem , Animals , Phylogeny , China , Birds/genetics
TiO2 is a commonly used material to remove arsenic from drinking water by adsorption as well as photocatalytic oxidation (PCO). In the present paper, arsenic adsorption and PCO at different pH environments are studied on the (1 1 0) facet of rutile TiO2 (r-TiO2). A self-competitive adsorption (SCA) behavior of arsenic is observed; i.e., arsenic species compete to adsorb on the surface. Related DFT calculations are carried out to simulate adsorption. SCA behavior is the key to connecting calculation results with experimental results. Furthermore, PCO of arsenite is performed at different pH values. Of note, PCO is related to adsorption; namely, the adsorption process determines the whole PCO reaction speed. Therefore, SCA is also helpful for the PCO reaction. The SCA behavior is useful not only for arsenic on r-TiO2 but also for arsenic on anatase TiO2 (a-TiO2). It may be helpful to further study arsenic adsorption and PCO on other materials such as Fe2O3 and MnO2. The SCA behavior extends our understanding of arsenic and provides new insights into arsenic removal and its cycle in nature.
Photoelectrochemical (PEC) sensing enables the rapid, accurate, and highly sensitive detection of biologically important chemicals. However, achieving high selectivity without external biological elements remains a challenge because the PEC reactions inherently have poor selectivity. Herein, we report a strategy to address this problem by regulating the charge-transfer pathways using polymeric carbon nitride (pCN)-based heterojunction photoelectrodes. Interestingly, because of redox reactions at different semiconductor/electrolyte interfaces with specific charge-transfer pathways, each analyte demonstrated a unique combination of photocurrent-change polarity. Based on this principle, a pCN-based PEC sensor for the highly selective sensing of ascorbic acid in serum against typical interferences, such as dopamine, glutathione, epinephrine, and citric acid was successfully developed. This study sheds light on a general PEC sensing strategy with high selectivity without biorecognition units by engineering charge-transfer pathways in heterojunctions on photoelectrodes.
Ascorbic Acid , Citric Acid , Dopamine , Engineering
A maternal history of major depressive disorder (MDD) is a well-known risk factor for depression in offspring. However, the mechanism through which familial risk is transmitted remains unclear. Cognitive control alterations are common in MDD, and thus, the current study investigated whether altered control capacity is transmitted intergenerationally, and whether it then contributes to the developmental pathways through which depression is passed from mothers to children. We recruited children (N = 65) ages 4-10-years-old, of which 47.7 % (n = 31) reported a maternal history of MDD, and their biological mother (N = 65). Children performed a child-friendly Go/NoGo task while electroencephalography (EEG) data were recorded, and mothers performed a Flanker task. Children exhibited heightened sensitivity to error versus correct responses, which was characterized by an error-related negativity (ERN), error positivity (Pe) as well as prominent delta and frontal midline theta (FMT) oscillations. Interestingly, worse maternal performance on the Flanker task associated with an increased Go/NoGo error rate and a smaller ERN and Pe in children. However, there was no association between maternal or child control indices with child depression symptoms. Our results suggest a familial influence of cognitive control capacity in mother-child dyads, but it remains unclear whether this confers risk for depressive symptoms in children. Further research is necessary to determine whether alterations in cognitive control over time may influence symptom development in at-risk children.
Depressive Disorder, Major , Female , Humans , Child, Preschool , Child , Depression , Electroencephalography/methods , Mothers/psychology , Cognition , Evoked Potentials/physiology
A single stimulus leading to multiple responses is an essential function of many biological networks, which enable complex life activities. However, it is challenging to duplicate a similar chemical reaction network (CRN) using non-living chemicals, aiming at the disclosure of the origin of life. Herein, we report a nanozyme-based CRN with feedback and feedforward functions for the first time. It demonstrates multiple responses at different modes and intensities upon a single H2O2 stimulus. In the two-electron cascade oxidation of 3,3',5,5'-tetramethylbenzidine (TMB), the endogenous product H2O2 competitively inhibited substrates in the first one-electron oxidation reaction on a single-atom nanozyme (Co-N-CNTs) and strikingly accelerated the second one-electron oxidation reaction under a micellar nanozyme. As a proof-of-concept, we further confined the nanozymatic network to a microfluidic chip as a simplified artificial cell. It exhibited remarkable selectivity and linearity in the perception of H2O2 stimulus against more than 20 interferences in a wide range of concentrations (0.01-100 mM) and offered an instructive platform for studying primordial life-like processes.
Polymeric carbon nitride (pCN) has attracted increasing interest as a metal-free photocatalyst because of its high efficiency in reactive oxygen species (ROS) generation. However, due to poor solubility, compounding pCN at the molecular level into more advanced nanocomposites remains a challenge. Herein, we report the dissolution of pCN in polyphosphoric acid (PPA) for the first time and fluid-phase assembly with carbon nanotubes (CNTs) into a flexible free-standing membrane. Mechanism and generality studies disclosed that the coordination of the acidity, viscosity, and adsorption energy of the solvents led to the successful dissolution of pCN. Interestingly, the pCN/CNTs molecular composite membrane exhibited not only superior mechanical properties and cycling performance as a result of strengthened π-π interfacial interaction, but also outstanding inactivation of E. coli and S. aureus in sterilization and wound healing for laboratory mice via photogenerated oxygen radicals. It would open a new era of pCN for biomedical applications in molecular composite membranes, beyond the traditional solar fuel applications in powders.
Self-adaptability is highly envisioned for artificial devices such as robots with chemical noses. For this goal, seeking catalysts with multiple and modulable reaction pathways is promising but generally hampered by inconsistent reaction conditions and negative internal interferences. Herein, we report an adaptable graphitic C6N6-based copper single-atom catalyst. It drives the basic oxidation of peroxidase substrates by a bound copper-oxo pathway, and undertakes a second gain reaction triggered by light via a free hydroxyl radical pathway. Such multiformity of reactive oxygen-related intermediates for the same oxidation reaction makes the reaction conditions capable to be the same. Moreover, the unique topological structure of CuSAC6N6 along with the specialized donor-π-acceptor linker promotes intramolecular charge separation and migration, thus inhibiting negative interferences of the above two reaction pathways. As a result, a sound basic activity and a superb gain of up to 3.6 times under household lights are observed, superior to that of the controls, including peroxidase-like catalysts, photocatalysts, or their mixtures. CuSAC6N6 is further applied to a glucose biosensor, which can intelligently switch sensitivity and linear detection range in vitro.
Copper , Graphite , Copper/chemistry , Oxidation-Reduction , Catalysis , Peroxidase , Peroxidases , Free Radicals , Graphite/chemistry , Reactive Oxygen Species
The enzyme-free catalytic hairpin assembly (CHA) process is introduced as a functional reaction module for guided, high-throughput, emergence, and evolution of constitutional dynamic networks, CDNs, from a set of nucleic acids. The process is applied to assemble networks of variable complexities, functionalities, and spatial confinement, and the systems provide possible mechanistic pathways for the evolution of dynamic networks under prebiotic conditions. Subjecting a set of four or six structurally engineered hairpins to a promoter P1 leads to the CHA-guided emergence of a [2 × 2] CDN or the evolution of a [3 × 3] CDN, respectively. Reacting of a set of branched three-arm DNA-hairpin-functionalized junctions to the promoter strand activates the CHA-induced emergence of a three-dimensional (3D) CDN framework emulating native gene regulatory networks. In addition, activation of a two-layer CHA cascade circuit or a cross-catalytic CHA circuit and cascaded driving feedback-driven evolution of CDNs are demonstrated. Also, subjecting a four-hairpin-modified DNA tetrahedron nanostructure to an auxiliary promoter strand simulates the evolution of a dynamically equilibrated DNA tetrahedron-based CDN that undergoes secondary fueled dynamic reconfiguration. Finally, the effective permeation of DNA tetrahedron structures into cells is utilized to integrate the four-hairpin-functionalized tetrahedron reaction module into cells. The spatially localized miRNA-triggered CHA evolution and reconfiguration of CDNs allowed the logic-gated imaging of intracellular RNAs. Beyond the bioanalytical applications of the systems, the study introduces possible mechanistic pathways for the evolution of functional networks under prebiotic conditions.
Biosensing Techniques , DNA, Catalytic , Nanostructures , DNA, Catalytic/chemistry , Feedback , DNA/chemistry , Nanostructures/chemistry , Catalysis
In this paper, the temperature performance of AlN-SAW resonators and AlScN-SAW resonators is studied. They are simulated by COMSOL Multiphysics, and their modes and the S11 curve are analyzed. The two devices were fabricated using MEMS technology and tested using VNA, and the test results were consistent with the simulation results. Temperature experiments were carried out with temperature control equipment. With the change in temperature, the changes in S11 parameters, TCF coefficient, phase velocity, and quality factor Q were analyzed. The results show that the temperature performance of the AlN-SAW resonator and the AlScN-SAW resonator is very good, and both have good linearity. At the same time, the sensitivity of the AlScN-SAW resonator is greater by 9.5%, the linearity is greater by 15%, and the TCF coefficient is greater by 11.1%. The temperature performance is excellent, and it is more suitable as a temperature sensor.
The development of electrochemiluminescence (ECL) emitters of different colors with high ECL efficiency (ΦECL) is appealing yet challenging for ultrasensitive multiplexed bioassays. Herein, we report the synthesis of highly efficient polymeric carbon nitride (CN) films with fine-tuned ECL emission from blue to green (410, 450, 470, and 525 nm) using the precursor crystallization method. More importantly, naked eye-observable and significantly enhanced ECL emission was achieved, and the cathodic ΦECL values were ca. 112, 394, 353, and 251 times those of the aqueous Ru(bpy)3Cl2/K2S2O8 reference. Mechanism studies showed that the density of surface-trapped electrons, the associated nonradiative decay pathways, and electron-hole recombination kinetics were crucial factors for the high ΦECL of CN. Based on high ΦECL and different colors of ECL emission, the wavelength-resolved multiplexing ECL biosensor was constructed to simultaneously detect miRNA-21 and miRNA-141 with superior low detection limits of 0.13 fM and 25.17 aM, respectively. This work provides a facile method to synthesize wavelength-resolved ECL emitters based on metal-free CN polymers with high ΦECL for multiplexed bioassays.
Biosensing Techniques , MicroRNAs , Luminescent Measurements/methods , Electrochemical Techniques/methods , Photometry , Biosensing Techniques/methods , Polymers
CTG (Cardiotocography) is an effective tool for fetal status assessment. Clinically, doctors mainly evaluate the health of fetus by observing FHR (fetal heart rate). The rapid development of Artificial Intelligence has led realization of computer-aided CTG technology, Intelligent CTG classification based on FHR is a fundamental component of these technologies. Its implementation can provide doctors with auxiliary decisions. Most of existing FHR classification methods are based on combing different deep learning models, such as CNN (Convolutional Neural Network), LSTM (Long short-term memory) and Transformer. However, these studies ignore the balance of positive and negative samples in dataset and the matching degree between model and FHR classification task, which reduces the classification accuracy. In this paper, we mainly discuss two major problems in previous FHR classification studies: reduce class imbalance and select appropriate convolution kernel. To address above two problems, we propose a data augmentation method based on ECMN (Edge Clipping and Multiscale Noise) to resolve class imbalance. Subsequently, we introduce a one-dimensional long convolutional layer, which use trend area to calculate the appropriate convolution kernel. Based on appropriate convolution kernel, an improved residual structure with attention mechanism named TGLCN (Trend-Guided Long Convolution Network) is proposed to improve FHR classification accuracy. Finally, horizontal and longitudinal experiments show that the TGLCN obtains high classification accuracy and speed of parameter adjustment.
Artificial Intelligence , Deep Learning , Pregnancy , Female , Humans , Heart Rate, Fetal/physiology , Neural Networks, Computer , Fetus/diagnostic imaging , Fetus/physiology
Fetal distress is a symptom of fetal intrauterine hypoxia, which is seriously harmful to both the fetus and the pregnant woman. The current primary clinical tool for the assessment of fetal distress is Cardiotocography (CTG). Due to subjective variability, physicians often interpret CTG results inconsistently, hence the need to develop an auxiliary diagnostic system for fetal distress. Although the deep learning-based fetal distress-assisted diagnosis model has a high classification accuracy, the model not only has a large number of parameters but also requires a large number of computational resources, which is difficult to deploy to practical end-use scenarios. Therefore, this paper proposes a lightweight fetal distress-assisted diagnosis network, LW-FHRNet, based on a cross-channel interactive attention mechanism. The wavelet packet decomposition technique is used to convert the one-dimensional fetal heart rate (FHR) signal into a two-dimensional wavelet packet coefficient matrix map as the network input layer to fully obtain the feature information of the FHR signal. With ShuffleNet-v2 as the core, a local cross-channel interactive attention mechanism is introduced to enhance the model's ability to extract features and achieve effective fusion of multichannel features without dimensionality reduction. In this paper, the publicly available database CTU-UHB is used for the network performance evaluation. LW-FHRNet achieves 95.24% accuracy, which meets or exceeds the classification results of deep learning-based models. Additionally, the number of model parameters is reduced many times compared with the deep learning model, and the size of the model parameters is only 0.33 M. The results show that the lightweight model proposed in this paper can effectively aid in fetal distress diagnosis.
Emerging as cost-effective potential alternatives to natural enzymes, nanozymes have attracted increasing interest in broad fields. To exploit the in-depth potential of nanozymes, rational structural engineering and explicit catalytic mechanisms at the molecular scale are required. Recently, impressive progress has been made in mimicking the characteristics of natural enzymes by constructing metal active sites, binding pockets, scaffolds, and delicate allosteric regulation. Ingenious in-depth studies have been conducted with advances in structural characterization and theoretical calculations, unveiling the "black box" of nanozyme-catalytic mechanisms. This review introduces the state-of-art synthesis strategies by learning from the natural enzyme counterparts and summarizes the general overview of the nanozyme mechanism with a particular emphasis on the adsorbed intermediates and descriptors that predict the nanozyme activity The emerging activity assessment methodology that illustrates the relationship between electrochemical oxygen reduction and enzymatic oxygen reduction is discussed with up-to-date advances Future opportunities and challenges are presented in the end to spark more profound work and attract more researchers from various backgrounds to the flourishing field of nanozymes.
Electrostatic capacitors are emerging as a highly promising technology for large-scale energy storage applications. However, it remains a significant challenge to improve their energy densities. Here, an effective strategy of introducing non-isovalent ions into the BiFeO3 -based (BFO) ceramic to improve energy storage capability via delaying polarization saturation is demonstrated. Accordingly, an ultra-high energy density of up to 7.4 J cm-3 and high efficiency ≈ 81% at 680 kV m-1 are realized, which is one of the best energy storage performances recorded for BFO-based ceramics. The outstanding comprehensive energy storage performance is attributed to inhibiting the polarization hysteresis resulting from generation ergodic relaxor zone and random field, and generating highly-delayed polarization saturation with continuously-increased polarization magnitudes with the electric field of supercritical evolution. The contributions demonstrate that delaying the polarization saturation is a consideration for designing the next generation of lead-free dielectric materials with ultra-high energy storage performance.
Facile evaluation of oxygen reduction reaction (ORR) kinetics for electrocatalysts is critical for sustainable fuel-cell development and industrial H2 O2 production. Despite great success in ORR studies using mainstream strategies, such as the membrane electrode assembly, rotation electrodes, and advanced surface-sensitive spectroscopy, the time and spatial distribution of reactive oxygen species (ROS) intermediates in the diffusion layer remain unknown. Using time-dependent electrochemiluminescence (Td-ECL), we report an intermediate-oriented method for ORR kinetics analysis. Owing to multiple ultrasensitive stoichiometric reactions between ROS and the ECL emitter, except for electron transfer numbers and rate constants, the potential-dependent time and spatial distribution of ROS were successfully obtained for the first time. Such exclusively uncovered information would guide the development of electrocatalysts for fuel cells and H2 O2 production with maximized activity and durability.
Electrochemiluminescence (ECL) has several advantages, such as a near-zero background signal, high sensitivity, wide dynamic range, simplicity, and is widely used for sensing, imaging, and single cell analysis. ECL luminophores are the key factors in the performance of various applications. Among various luminophores, small organic luminophores exhibit many intriguing features including good biocompatibility, facile modification, well-defined molecular structure, and sustainable raw materials, making small organic luminophores attractive for the use in the ECL field. Although many great achievements have been made in the synthesis of new small organic luminophores, solving various challenges, and expanding new applications, there are almost no comprehensive reviews on small organic ECL luminophores. In this review, we briefly introduce the advantages and emission mechanisms of small organic ECL luminophores, summarize the main types, molecular characteristics, and ECL properties of most existing small organic ECL luminophores, and present the important applications and design principles in sensors, imaging, single cell analysis, sterilization, and other fields. Finally, the challenges and outlook of organic ECL luminophores to be popularized in biosensing applications are also discussed.
Biosensing Techniques , Luminescent Measurements , Luminescent Measurements/methods , Biosensing Techniques/methods , Photometry , Electrochemical Techniques/methods
