Search Results (4,648)

Wearable systems are increasingly adopted for health monitoring and wellness promotion. Among the most relevant biosignals, the electrocardiogram (ECG) plays a key role; however, in wearable settings (e.g., during physical activity), it is often corrupted by electromyogram (EMG) interference. This study presents a novel adaptive algorithm, template masking (TM), which integrates the stationary wavelet transform (SWT) with template matching for denoising the ECG from EMG. The method identifies the optimal wavelet and decomposition level to maximise detail sparsity. To mitigate EMG interference, after alignment in the SWT domain with a template, the detail coefficients are multiplied by a binary mask and smoothed. TM was compared with soft and hard thresholding on (1) simulations combining clinical ECGs (MIT-BIH database) and synthetic EMGs with different signal-to-noise ratios (SNRs), and (2) experimental signals including ECGs acquired with dry electrodes corrupted by EMGs (SimEMG database, also varying SNRs), as a potential wearable scenario. In both cases, TM yielded significantly lower reconstruction errors at SNRs below 5 dB ($p<0.01$) and significantly outperformed thresholding in the sensitivity of R-peaks estimation ($p<0.001$). These results demonstrate the potential of TM, highlighting the value of adaptive denoising algorithms. Full article

Currently, flexible sensors based on electrochemical principles are predominantly limited to single-parameter detection, making it challenging to meet the demand for synchronous monitoring of multiple analytes in complex physiological environments. This study presents a 3D-printed flexible sensor for synchronous glucose/pH detection. Glucose was quantified via H₂O₂ oxidation current (GOD-catalyzed reaction), while pH was measured through polyaniline (PANI) resistance changes. The ionogel-based microneedle electrode ensures mechanical robustness. At 0.2 V, optimal signal decoupling was achieved: glucose oxidation current dominates, while PANI’s polarization effect is minimized. Neutral pH minimally affected glucose oxidase (GOD) activity, and low glucose concentrations induced negligible pH interference, ensuring orthogonality. In artificial interstitial fluid, the sensor showed glucose: linear response (0.5–2.5 g·L⁻¹, 0.288 μA·mM⁻¹·cm⁻²); pH: piecewise-linear sensitivity (0.155 Ω/pH·cm² for pH > 7; 0.135 Ω/pH·cm² for pH < 7). The design enables real-time multiparameter monitoring with high selectivity, addressing current limitations in flexible electrochemical sensors. Full article

The development of ternary metal oxide electrocatalysts with optimized electronic structures and surface morphologies has emerged as one of the effective strategies to improve the performance of electrochemical water splitting. In this work, ternary CoMoCe (CMC)-oxide electrocatalysts were successfully synthesized on nickel foam substrates via a hydrothermal technique and employed for their catalytic activity in an alkaline electrolyte. For comparison, binary counterparts (CoMo, CoCe, and MoCe) were also fabricated under similar conditions. The synthesized catalysts’ electrodes exhibited diverse surface architectures, including microporous-flake hybrids, ultrathin flakes, nanoneedle-assembled microspheres, and randomly oriented hexagonal structures. Among them, the ternary CoMoCe-oxide electrode exhibited outstanding bifunctional electrocatalytic activity, delivering low overpotentials of 124 mV for the hydrogen evolution reaction (HER) at −10 mA cm⁻², and 340 mV for the oxygen evolution reaction (OER) at 100 mA cm⁻², along with excellent durability. Furthermore, in full water-splitting configuration, the CMC||CMC and RuO₂||CMC electrolyzers required cell voltages of 1.69 V and 1.57 V, respectively, to reach a current density of 10 mA cm⁻². Remarkably, the CMC-based electrolyzer reached an industrially relevant current density of 1000 mA cm⁻² at a cell voltage of 2.18 V, maintaining excellent stability over 100 h of continuous operation. These findings underscore the impact of an optimized electronic structure and surface architecture on design strategies for high-performance ternary metal oxide electrocatalysts. Herein, a robust and straightforward approach is comprehensively presented for fabricating highly efficient ternary metal-oxide catalyst electrodes, offering significant potential for scalable water splitting. Full article

This study deciphers the anionic modulation mechanism of halide ions (F⁻/Cl⁻) in cobalt-based hydroxides for oxygen evolution reaction (OER). Phase-pure Co(OH)₂, Co(OH)F, and Co₂(OH)₃Cl were fabricated via substrate-independent hydrothermal synthesis to eliminate conductive support interference. Electrocatalytic evaluation on glassy carbon electrodes demonstrates fluoride’s superior regulatory capability over chloride. X-ray photoelectron spectroscopy (XPS) analyses revealed that F⁻ incorporation induces charge redistribution through Co → F electron transfer, optimizing the electronic configuration via ligand effects. F⁻ incorporation simultaneously guided the anisotropic growth of 1D nanorods and reduced surface energy, thereby enhancing the wettability of Co(OH)F. The engineered Co(OH)F catalyst delivers exceptional OER performance: 318 mV overpotential at 10 mA/cm² in 1 M KOH with 94% current retention over 20 h operation. This study provides a synthetic strategy for preparing pure-phase Co(OH)F and compares halide ions’ effects on enhancing OER activity through electronic structure modulation and morphological control of basic cobalt salts. Full article

Synaptic activity is fundamental to memory and learning in the nervous system. However, most artificial synaptic devices are limited to mimicking static plasticity, and tunable plasticity has not been achieved at the device level. Here, we introduce a dynamic all-optical synapse based on photonic crystal nanobeam cavities with a ferroelectric carrier injection valve. By leveraging the nonlinear and ferroelectric electrostatic doping effects in silicon, integrated with Hf_0.5Zr_0.5O₂ (HZO) film as the ferroelectric layer and indium tin oxide (ITO) as the top electrode, we enhance linearity and reduce power consumption. Increasing the bias voltage further improves linearity while decreasing power consumption. This innovation offers a promising pathway for developing energy-efficient nanophotonic devices in neuromorphic computing. Full article

The development of efficient non-noble metal catalysts is critical for advancing sustainable fuel-cell technologies. This study investigates the effect of carbon support microstructure on the oxygen reduction reaction (ORR) performance of Fe-N-C catalysts. By precisely tuning the pyrolysis temperature of activated carbon (AC) between 600 and 1000 °C, we elucidate the mechanistic influence of the physicochemical characteristics of the carbon support on the ORR activity of the supported catalyst. Increasing the pyrolysis temperature enhanced the electrical conductivity of the carbon support, thereby improving the ORR performance of the catalyst. However, while the defect density and specific surface area of the carbon support initially increased with increasing pyrolysis temperature, they declined when elevated temperatures were used (e.g., 1000 °C), leading to reduced ORR activity. The AC-900 support, pyrolyzed at 900 °C, exhibited an optimal balance of a high surface area, abundant defects, and superior conductivity. An Fe phthalocyanine/AC-900 catalyst synthesized using the AC-900 support exhibited excellent ORR activity (E_1/2: 0.89 V and E_on: 0.95 V vs. reversible hydrogen electrode (RHE)) in 0.1 M KOH. This work highlights the pivotal role of carbon support microstructure in governing the ORR activity of the supported catalyst and provides a rational strategy for designing high-performance non-noble metal electrocatalysts. Full article

We have demonstrated the fabrication of laminate composites with functional features to demonstrate energy storage capabilities. The present study investigates the surface modification of carbon fibers by coating dual materials of reduced graphene oxide (rGO) and cellulose-based activated carbon to enhance their energy storage capacitance for the development of structural supercapacitors. The dual coating on carbon fibers enabled a near 210-fold improvement in surface area, surpassing that of pristine carbon fibers. This formed a highly porous graphene network with activated carbon, resulting in a well-connected fiber–graphene-activated carbon network on carbon fibers. The electrochemical supercapacitor, fabricated from surface-functionalized carbon fibers, provides the best performance, with a specific capacitance of 172 F g⁻¹ in an aqueous electrolyte. Furthermore, the symmetrical structural supercapacitor (SSSC) device delivered a specific capacitance of 227 mF g⁻¹ across a wide potential window of 6 V. The electrochemical stability of the SSSC device was validated by a high capacitance retention of 97.3% over 10,000 cycles. Additionally, the study showcased the practical application of this technology by successfully illuminating an LED using the proof-of-concept SSSC device with G-aC/CF electrodes. Overall, the findings of this study highlight the potential of carbon fiber composites as a promising hybrid material, offering both structural integrity and a functional performance suitable for aerospace and automobile applications. Full article

The rapid rise of antibiotic-resistant bacteria (ABR) presents an urgent global health challenge, necessitating the development of efficient and scalable diagnostic technologies. Electrochemical biosensors have emerged as a promising solution, offering high sensitivity, specificity, and adaptability for point-of-care applications. These innovative platforms utilize bio-recognition elements, advanced electrode materials, microbial enzymes, and redox-active metabolites to identify antibiotic resistance profiles at a molecular level. Recent progress in microfluidics and lab-on-a-chip systems has enabled real-time, high-throughput antimicrobial susceptibility testing, significantly improving diagnostic precision and speed. This review aims to critically evaluate recent advances in electrochemical biosensing strategies for detecting ABR, identify key challenges, and propose future directions to enhance clinical applicability. Key developments include bio-receptor-based detection strategies, novel electrode surfaces, and multiplexed platforms integrated with microfluidic systems. Additionally, this review examines essential biomarkers for detecting antibiotic resistance and explores key challenges, including variability in biomarker expression and sensor reproducibility. It also highlights practical barriers to clinical implementation, such as cost constraints and scalability concerns. By presenting innovative approaches, such as cost-effective material alternatives, advanced analytical techniques, and portable biosensing systems, this review outlines a strategic pathway for enhancing the accessibility and effectiveness of electrochemical biosensors in antibiotic resistance management. Full article

This work successfully achieved pre-intercalation of Fe species in V2CTx MXene through an annealing method. The crystallographic structure, microscopic morphology, and functional groups of the samples before and after pre-intercalation were analyzed by XRD, SEM, and FTIR, and the electrochemical performance of MXene electrodes was studied. Research has shown that the interlayer spacing of pre-intercalated MXene increases with an increase in annealing temperature. The interlayer spacing of MXene annealed at 800 °C is 13.1% higher than that of the original MXene. However, the morphology of the samples was damaged by excessively high annealing temperatures, which also weakened the lithium-ion storage performance. In contrast, the cycling performance of MXene electrodes annealed at 400 °C showed the greatest improvement, reaching 71.65%. This is because iron species, acting as a pillar support structure, expand the interlayer spacing and broaden the transport channels for lithium ions. Meanwhile, high-temperature annealing generates more oxygen-containing functional groups, which provide additional active sites for lithium-ion transport, promote the kinetics of electrode reactions, and thus enhance its lithium-ion storage performance. Full article

Motor impairments significantly affect individuals’ ability to perform activities of daily living, reducing autonomy and quality of life. In response to this, robot-assisted rehabilitation has emerged as an effective and practical solution, enabling controlled limb movements and supporting functional recovery. This study presents the development of an upper-limb exoskeleton designed to assist rehabilitation by integrating neurophysiological signal processing and real-time control strategies. The system incorporates a proportional–derivative (PD) controller to execute cyclic flexion and extension movements based on a sinusoidal reference signal, providing repeatability and precision in motion. The exoskeleton integrates a brain–computer interface (BCI) that utilizes electroencephalographic signals for therapy selection and engagement enabling user-driven interaction. The EEG data extraction was possible by using the UltraCortex Mark IV headset, with electrodes positioned according to the international 10–20 system, targeting alpha-band activity in channels O1, O2, P3, P4, Fp1, and Fp2. These channels correspond to occipital (O1, O2), parietal (P3, P4), and frontal pole (Fp1, Fp2) regions, associated with visual processing, sensorimotor integration, and attention-related activity, respectively. This approach enables a more adaptive and personalized rehabilitation experience by allowing the user to influence therapy mode selection through real-time feedback. Experimental evaluation across five subjects showed an overall mean accuracy of 86.25% in alpha wave detection for EEG-based therapy selection. The PD control strategy achieved smooth trajectory tracking with a mean angular error of approximately 1.70°, confirming both the reliability of intention detection and the mechanical precision of the exoskeleton. Also, our core contributions in this research are compared with similar studies inspired by the rehabilitation needs of stroke patients. In this research, the proposed system demonstrates the potential of integrating robotic systems, control theory, and EEG data processing to improve rehabilitation outcomes for individuals with upper-limb motor deficits, particularly post-stroke patients. By focusing the exoskeleton on a single degree of freedom and employing low-cost manufacturing through 3D printing, the system remains affordable across a wide range of economic contexts. This design choice enables deployment in diverse clinical settings, both public and private. Full article

This research presents an experimental analysis of the influence of atmospheric pressure plasma on the performance of a micro horizontal-axis wind turbine blade. The investigation was conducted using an NACA 4412 airfoil equipped with a dielectric barrier discharge (DBD) plasma actuator. The electrodes were configured asymmetrically, with a 2 mm gap and copper electrodes that are 0.20 mm in thickness. A high voltage of 6 kV was applied, resulting in a current of 0.071 mA and a power output of 0.426 W. Optical emission spectroscopy identified the excited components through the interaction of the high-voltage AC electric field with air molecules: ${\mathrm{N}}_2$, ${\mathrm{N}}_2^{+}$, ${\mathrm{O}}_2^{+}$, and O. The electrohydrodynamic force mainly results from the observed charged ions that, when accelerated by the electric field, transfer momentum to neutral molecules via collisions, leading to the formation of the observed jet plasma. The findings indicated a notable enhancement in aerodynamic performance attributable to the electrohydrodynamic (EHD) flow generated by the plasma. The estimated electrohydrodynamic force ($8.712\times {10}^{-4}$ N) is capable of maintaining the flow attached to the airfoil surface, thereby augmenting flow circulation and, consequently, enhancing the lift force. According to blade element theory, the lift and drag coefficients directly influence the torque and mechanical power generated by the wind turbine rotor. Schlieren imaging was utilized to observe alterations in air density and flow patterns. Lissajous curve analysis was used to examine the electrical discharge behavior, showing that only 7.04% of the input power was converted into heat. This indicates that nearly all input electric energy was transformed into EHD force by the atmospheric pressure plasma. Compared to traditional aerodynamic control methods, DBD actuators are a feasible alternative for small wind turbines due to their lightweight design, absence of moving parts, ability to be surface-embedded without altering blade geometry, and capacity to generate active, dynamic flow control with reduced energy consumption. Full article

Hydrogen generation has become a popular research subject in light of currently pressing issues, such as the rapidly increasing environmental pollution, the depleting fossil fuel reserves, and the looming energy crisis. Sustainable electrochemical water splitting is regarded as one of the most desirable methods for obtaining green hydrogen. Considering this state of affairs, the water splitting electrocatalytic activity of glassy carbon electrodes modified with birnessite-type K₂Mn₄O₈ and mixed-valence iron phosphate Fe₃(PO₃OH)₄(H₂O)₄ materials were evaluated in electrolyte solutions having different pH values. Both compounds were characterized by X-ray diffraction and FT-IR spectroscopy in order to analyze their phase purity and their structural features. The most catalytically active birnessite-type K₂Mn₄O₈-based electrode was manufactured using a catalyst ink containing only the electrocatalyst dispersed in ethanol and Nafion solution. In 0.1 M H₂SO₄, it exhibited an oxygen evolution reaction (OER) overpotential of 1.07 V and a hydrogen evolution reaction (HER) overpotential of 0.957 V. The Tafel slopes obtained in the OER and HER experiments were 0.180 and 0.142 V/dec, respectively. The most catalytically active mixed-valence iron phosphate Fe₃(PO₃OH)₄(H₂O)₄-based electrode was obtained with a catalyst ink containing the specified material mixed with carbon black and dispersed in ethanol and Nafion solution. In a strongly alkaline medium, it displayed a HER overpotential of 0.515 V and a Tafel slope value of 0.122 V/dec. The two electrocatalysts have not been previously investigated in this way, and the acquired data provide insights into their electrocatalytic activity and improve the scientific understanding of their properties and applicative potential. Full article

This study presents a novel deep learning framework for classifying visual images based on brain responses recorded through electroencephalogram (EEG) signals. The primary challenge in EEG-based visual pattern recognition lies in the inherent spatiotemporal variability of neural signals across different individuals and recording sessions, which severely limits the generalization capabilities of classification models. Our work specifically addresses the task of identifying which image category a person is viewing based solely on their recorded brain activity. The proposed methodology incorporates three primary components: first, a brain hemisphere asymmetry-based dimensional reduction approach to extract discriminative lateralization features while addressing high-dimensional data constraints; second, an advanced channel selection algorithm utilizing Fisher score methodology to identify electrodes with optimal spatial representativeness across participants; and third, a Dynamic Temporal Warping (DTW) alignment technique to synchronize temporal signal variations with respect to selected reference channels. Comprehensive experimental validation on a visual image classification task using a publicly available EEG-based visual classification dataset, ImageNet-EEG, demonstrates that the proposed disambiguation framework substantially improves classification accuracy while simultaneously enhancing model convergence characteristics. The integrated approach not only outperforms individual component implementations but also accelerates the learning process, thereby reducing training data requirements for EEG-based applications. These findings suggest that systematic spatiotemporal disambiguation represents a promising direction for developing robust and generalizable EEG classification systems across diverse neurological and brain–computer interface applications. Full article

This study employed the solid-state method to prepare perovskite-type high-entropy oxide materials La(Co_0.2Cr_0.2Fe_0.2Mn_0.2Ni_0.2)O₃ and La(Co_0.2Cr_0.2Ni_0.2Ga_0.2Ge_0.2)O₃ with equimolar ratios at the B-site and explored the effects of sintering temperature on the phase structure and electrochemical properties of high-entropy oxide ceramics. The results show that after sintering at 1300$°\mathrm{C}$, both samples exhibit orthorhombic perovskite structures. Both have a relative density of >97%, while La(Co_0.2Cr_0.2Ni_0.2Ga_0.2Ge_0.2)O₃ has a significantly larger grain size. Using these materials as electrodes, the cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) results indicate that the working electrode made of La(Co_0.2Cr_0.2Ni_0.2Ga_0.2Ge_0.2)O₃ shows higher oxidation reaction activity in CV measurements and achieved a specific capacitance of 74.3 F/g at a current density of 1 A/g in GCD measurements, which still maintained 73% of its initial specific capacitance (54.3 F/g) when the current density was increased to 10 A/g. Its capacitance retention rate is 10 percentage points higher than that of La(Co_0.2Cr_0.2Fe_0.2Mn_0.2Ni_0.2)O₃ at high current densities, demonstrating superior rate performance. Full article

Uric acid (UA) detection is critical for human health monitoring, necessitating the development of electrochemical sensing electrodes suitable for physiological environments. This study evaluated four 2D conductive metal–organic frameworks (2D c-MOFs), namely Cu-HHTP, Ni-HHTP, Cu-HAB, and Ni-HAB, which share identical graphene-like 2D sheet structures but differ in π-conjugation extent and catalytic active centers [MX₄] (M = Cu or Ni; X = O or NH) as electrosensing electrodes. Electrochemical sensing performance was compared by detecting UA in phosphate-buffered saline (PBS). Herein, the Ni-HHTP electrode demonstrated superior sensitivity (6.79 μA·μM⁻¹·cm⁻²), the lowest oxidation potential (0.272 V), and the lowest detection limit (0.44 μM). Langmuir adsorption isotherm analysis revealed that the Ni-HHTP electrode possesses the highest surface coverage (Γ_A) (5061.16 pmol cm⁻²) and the most favorable Gibbs adsorption free energy (ΔG°) (−18.775 kJ mol⁻¹), indicating its strongest UA adsorption capacity and molecular interaction. This enhanced performance is attributed to the optimal synergy between [NiO₄] catalytic centers and extended ligand π-conjugation, facilitating greater analyte adsorption and electron transfer efficiency. This work establishes clear structure–performance relationships for 2D c-MOF electrodes in UA detection, providing key insights for designing advanced electrosensing materials. Full article