Search Results (4,516)

Hydrogels are highly hydrated polymeric soft materials that closely mimic the mechanical characteristics of biological soft tissues, offering immense promise for biomedical applications such as tissue engineering, drug delivery, flexible electronics, and wound repair. Conventional hydrogels crosslinked by static covalent bonds suffer from irreversible network disruption upon mechanical damage and lack active responsiveness to environmental stimuli, severely limiting their practical use. Incorporating both dynamic covalent bonds and dynamic non-covalent interactions endows hydrogels with intelligent features, including self-healing, injectability, and stimuli responsiveness. A single dynamic crosslinking mode often fails to achieve an optimal balance between mechanical robustness and rapid dynamic reversibility, whereas the synergistic integration of multiple dynamic bonds—exploiting their complementarity in kinetics, energy dissipation, and stimuli-responsiveness—has emerged as a cutting-edge strategy to overcome this limitation. Using dynamic non-covalent interactions as the classification framework, this review systematically summarizes recent advances in hydrogels crosslinked by combinations of dynamic non-covalent interactions with dynamic covalent bonds, covering hydrogen bonds, host–guest interactions, metal–ligand interactions, electrostatic interactions, hydrophobic interactions, π–π stacking interactions, and other emerging multiple dynamic crosslinking systems. The design principles, synergistic mechanisms, multifunctional applications, and key challenges and future directions of each system are discussed and prospected. Full article

The rapid development of wearable electronics has created a demand for flexible, lightweight, and sustainable power-supply technologies. The persistent temperature difference between the human body and the environment provides a low-grade thermal source for thermoelectric energy harvesting. However, traditional flexible thermoelectric devices still face limited self-supporting capabilities and difficulties in maintaining sufficiently low cold-side temperatures. Here, we designed a passively radiative-cooled thermoelectric film (PRT film) by integrating a PAM/PEDOT:PSS self-supporting thermoelectric composite layer with a polymer metamaterial radiative cooling (PMRC) film. The PAM/PEDOT:PSS layer serves as a self-supporting thermoelectric conversion component for harvesting low-grade heat, while the PMRC film layer acts as a passive cold-side regulator without energy input to lower the cold-side temperature and enhance the temperature gradient. By optimizing the PAM content, the PAM/PEDOT:PSS composite material with 85 wt% PAM achieved the highest power factor of 72.3 μW m⁻¹ K⁻². Under a temperature difference of 39 °C, the optimized PAM/PEDOT:PSS sample provided an open-circuit voltage of 0.47 V, a maximum output power of 1.1 μW, and a power density of 11.2 μW cm⁻². According to the temperature-difference enhancement measured in experiments and the independently obtained load characteristics, the integration of PMRC films is expected to increase the maximum output power from 1.1 to 1.4 μW, with the corresponding power density rising from 11.2 to 14.25 μW cm⁻², representing a 27.2% enhancement. This work demonstrates the feasibility of passive radiative cold-side regulation in enhancing low-level thermoelectric energy harvesting for wearable applications. Full article

In order to provide a theoretical framework for the synergistic optimization of the “rigid backbone-flexible network” in the molecular design of polyimides with high Young’s modulus, high toughness, and excellent creep resistance for wearable electronics applications, the effects of sequence structure on the mechanical properties of siloxane-containing polyimides were investigated by molecular dynamics simulations. A series of poly(siloxane-imide) block copolymer models with distinct sequence structures were constructed via molecular dynamics (MD) simulations based on 4,4′-(hexafluoroisopropylidene)diphthalic anhydride (6FDA) and 2,2′-bis(trifluoromethyl)benzidine (TFDB) as hard segment A, and 6FDA and 1,3-bis(3-aminopropyl)tetramethyldisiloxane (SiDA) as soft segment B. The results indicate that extending hard segment length enhances Young’s modulus and suppresses creep because of the enhancement of chain rigidity and formation of stable physical aggregates. Appropriately extending soft segment sequence length can improve the failure strain through rapid conformational adjustment, while excessively long soft segments lead to stress concentration, thereby reducing the failure strain. The (A₅B₅)₂ model structure exhibits superior comprehensive performance among all systems, with a relatively high Young’s modulus, failure strain, and creep recovery rate. This is attributed to the synergistic balance between the rigidity of the hard segment and the mobility of the soft segment. Full article

Flexible strain sensors require both a responsive conductive network and a mechanically compliant supporting structure. In this study, a sandwich-structured carbon nanotube (CNT)/graphene–thermoplastic polyurethane (TPU) nanofiber strain sensor was fabricated by electrospinning, spray-coating, pre-stretching, and hot-pressing. Field-emission scanning electron microscopy was used to examine the nanofiber and coated-fiber morphology, while energy-dispersive X-ray spectroscopy was used only to describe elemental distribution. The electrical response was quantitatively evaluated under tensile deformation. The sensor exhibited piecewise gauge factors of approximately 47.3, 269.6, and 613.8 over strain ranges of 0–20%, 20–45%, and 45–60%, respectively. The response and recovery times were approximately 120 and 180 ms, and the electrical response remained observable over 5000 loading–unloading cycles at 20% strain. Qualitative demonstrations involving finger-bending, elbow-bending, pulse, grasping, walking, and repeated pressing produced distinguishable resistance-time patterns. These results indicate the potential of the CNT/graphene–TPU device for wearable deformation monitoring. Full article

Metal parts produced by directed energy deposition go through a complex thermal history during the deposition stage, which can result in heterogeneous microstructures and the accumulation of residual stress. While individual post-processing heat treatments are widely used to address these issues, the industrial logistics of manufacturing large components often demand specific sequences of combined treatments (e.g., applying stress relief prior to substrate detachment to prevent distortion, followed by high-temperature solubilization, or vice versa). The microstructural and mechanical consequences of altering this sequence remain underexplored. Therefore, this study aimed to investigate the effects of different post-processing heat treatment sequences on the mechanical properties of AISI 316L deposited through laser-directed energy deposition. Tensile and Charpy impact tests were carried out on the specimens under five conditions: (i) as-built; (ii) stress relief; (iii) solubilization; (iv) stress relief and solubilization; and (v) solubilization and stress relief. A statistical analysis of variance supported a comparison between each treatment’s influence on the mechanical properties under each condition. Furthermore, the typical microstructures were assessed by optical microscopy, scanning electron microscopy (SEM) equipped with electron backscatter diffraction (EBSD), and X-ray diffraction (XRD). The solubilization treatment reduced the ultimate tensile strength (from ~618 MPa to ~576 MPa) and the yield stress (from ~424 MPa to ~299 MPa), while no significant change was observed in elongation (ranging from 27% to 38%) due to high data dispersion. The stress relief, however, did not significantly change these mechanical properties. Considering the heat treatment combinations, the solubilization had a stronger impact on tensile stress than the stress relief, regardless of the treatment order. Impact resistance was not significantly affected by any of the heat treatments, maintaining an average of ~114 J. The solubilization treatment fully recrystallized the microstructure, while the stress relief did not promote any significant changes at an optical microscopy level. Ultimately, this study demonstrates that the microstructural transformations induced by the solubilization step dominate the final mechanical baseline, indicating that the sequence order is not a determining factor. This finding grants critical flexibility for industrial manufacturing logistics, allowing stress relief to be strategically applied when most convenient for dimensional stability without compromising final part performance. Full article

Flexible capacitive humidity sensors are pivotal for next-generation wearable electronics and Internet of Things (IoT) applications. However, conventional devices suffer from severe salt leaching and delamination of hygroscopic sensing materials, alongside poor interfacial adhesion and mechanical fragility of metallic electrodes. Herein, an innovative in situ strategy is reported for constructing LiCl-CH₃COOK/laser-induced graphene (LIG) composite flexible electrodes via single-step laser direct writing. This approach simultaneously patterns three-dimensional (3D) porous LIG interdigitated networks on polyimide substrates and drives deep infiltration of the LiCl-CH₃COOK hygroscopic phase within the graphene pores. The 3D interconnected LIG skeleton not only provides abundant physical anchoring sites and rapid water vapor transport channels but also effectively suppresses the physical loss and leaching of the deliquesced salts through micro-nanoscale spatial confinement, yielding remarkable interfacial stability and cycling lifetime. Benefiting from the synergistic deliquescence of the composite salts, the sensor delivers an exceptional sensitivity of 65,570% (ΔC/C₀), moderate response/recovery times of 75/90 s, and ultralow hysteresis of 0.981%. Furthermore, the streamlined laser-scribing route replaces conventional costly microfabrication sequences, enabling low-cost, high-precision customization. Demonstrations in human respiration monitoring and smart agriculture validate the sensor’s superior reliability and practical applicability, establishing a novel pathway for miniaturized, highly integrated, and robust flexible humidity detection systems. Full article

In this study, bacitracin—a small-molecule antibacterial agent—was incorporated into the network structure of a type I collagen/bacterial cellulose (Col/BC) composite via in situ recombination and impregnation adsorption, leveraging the well-established drug-loading capacity and sustained-release characteristics of type I collagen. The obtained BA@Col/BC (BA@CBC) functional dressing features a well-defined nanoporous architecture, high porosity (83.9 ± 1.8%), rapid water absorption kinetics, substantial water absorption capacity (48.7 ± 2.4 g/g), and satisfactory moisture permeability (2984 ± 56 g·m²·day). Critically, the introduction of type I collagen not only effectively delays the release of bacitracin, but also significantly improves the cytocompatibility of the bacterial cellulose-based dressing. BA@CBC exhibits powerful antibacterial activities against S. aureus and MRSA, and can promote the proliferation of NIH3T3 and HUVEC, demonstrating excellent cytocompatibility. In vivo studies in a murine infected-wound model revealed that BA@CBC significantly accelerates wound closure compared to controls; histological evaluation further showed the most organized re-epithelialization, robust granulation tissue formation, and de novo hair follicle regeneration in the BA@CBC group. Fluorescence immunohistochemical analysis confirmed markedly reduced expression of pro-inflammatory cytokines IL-1β and TNF-α in BA@CBC-treated wounds, indicating effective suppression of excessive inflammation and consequent improvement of the wound microenvironment. Collectively, BA@CBC integrates favorable physicochemical properties, sustained antibacterial functionality, and superior cytocompatibility—positioning it as a promising candidate for clinical management of infected wounds. Full article

This study fabricated choline–acrylic acid deep eutectic solvent (DES) hydrogels via in situ free-radical polymerization and systematically investigated the individual and co-optimization effects of lignin dosage and water content on the chemical structure, micromorphology, compressive mechanical properties, swelling behavior, and thermal stability of the hydrogels. This work quantitatively uncovers the co-optimization mechanism between the two variables in modulating crosslink density and pore architecture, thereby filling a research gap in the dual-factor co-optimization of biomass-based DES hydrogels. The results reveal that a moderate lignin dosage (0.02 g) generates abundant dynamic hydrogen bonds, densifying the crosslinked network and raising the maximum compressive stress from 0.378 MPa to 0.426 MPa, whereas excessive lignin triggers molecular aggregation and deteriorates mechanical performance. Higher water content dilutes crosslinking sites, reduces network compactness, boosts the swelling ratio while lowering compressive strength, and exerts negligible impacts on thermal degradation characteristics. FTIR analysis confirms that lignin participates in network formation solely through non-covalent hydrogen bonds, without forming new covalent bonds. A comprehensive performance evaluation identifies the optimal formulation as 0.02 g lignin and 60 g water. Although this two-factor optimization strategy provides clear experimental and theoretical guidance for designing sustainable soft materials, the present work still has limitations, including the use of only static laboratory characterizations, with no cyclic mechanical measurements or aging assessments. This study advances the customized performance tuning of lignin-derived DES hydrogels and facilitates the high-value valorization of lignin, which is promising for multifunctional green-material applications, including adsorption, flexible electronics, and biological carriers. Full article

The small-data regime remains one of the most important and pressing problems in machine learning. With only a few hundred sample points in the training set, conventional ensemble methods are potentially unreliable because they have high variance, low generalization and are sensitive to individual points in the dataset, which can have a negative impact on any consumer application or safety-critical application. These problems are addressed by current regularization approaches one at a time, but there is no single approach that flexibly tunes the regularization level according to the statistics of the ensemble during training. This paper presents SelfReg-Ensemble, a variance-adaptive self-regularizing ensemble learning framework which tracks the variance at each training step of all base learners and dynamically adjusts the regularization intensity to keep the variance of the ensemble predictions at an acceptable level. The framework consists of three complementary components: (i) Variance Monitoring Module (VMM) to monitor the variance of predictions performed by the ensemble of members, (ii) Self-Regularization Controller (SRC) to adaptively map the observed variance to a regularization coefficient, using a sigmoid-bounded adaptive learning schedule, and (iii) Diversity-Preserving Aggregation Layer (DPAL) based on a weighted stacking with an entropy-regularized softmax voting mechanism, to avoid ensemble collapse. We offer rigorous theoretical analysis of the proposed framework that guarantees convergence and provides bounds on variance. These guarantees are formally established for convex, gradient-based learners; for the tree-based learners used in our experiments they serve as qualitative guidance and are supported empirically rather than formally proved. SelfReg-Ensemble is tested on 12 benchmark datasets from medical, financial and IoT domains, each with a total of fewer than 500 samples (N denotes total dataset size; effective per-fold training sizes Ntr range from 52 to 432 samples after stratified 90/10 splitting) and consistently outperforms ten state-of-the-art baselines, with on average 6.8% higher AUROC than XGBoost v2.0.3 and 5.2% higher than the actual strongest average baseline, Sub-Network Ensemble (85.6% average AUROC), 9.3% lower prediction variance, and 4.1% higher F1-score. The proposed framework is lightweight, modular and easily deployable in resource-limited consumer electronics environments. Full article

This work describes the design and characterization of nanocomposites based on multi-walled carbon nanotubes (MWCNTs) and commercial polymer matrices for innovative electronic applications. This work addresses the need for advanced materials for flexible electronics, sensing, and electromagnetic shielding. Sipolprene^® 25170-W, a flexible and durable polyester–polyether block copolymer, was used as the matrix. For filler incorporation, the commercial masterbatch Plasticyl™ PBT-1501 (15 wt% of MWCNTs in PBT, polybutylene terephthalate) was employed, ensuring operational safety and ease of dispersion. The samples were produced as films (with masterbatch contents ranging from 10% to 30% corresponding to a MWCNT content ranging from 1.5 wt% to 4.5 wt%) via twin-screw extrusion with a flat die. Characterization included SEM, FT-IR, TGA, DSC, tensile testing, surface wettability, volume resistivity measurements, and electro-mechanical tests. All the results confirmed good dispersion of the filler within the matrix: from a mechanical point of view, the addition of MWCNTs increased the Young’s modulus from 25 MPa of the neat material to 122 MPa of the material containing 4.5 wt% of MWCNTs, enhancing stiffness while maintaining good film handleability. Thermal analysis revealed the high stability of the obtained system and allowed us to identify the appropriate processing temperature parameters to guarantee the thermal stability of the materials during processing. Finally, electrical tests demonstrated a significant reduction in volume resistivity with increasing filler content: the volume resistivity decreased by about eleven orders of magnitude, from approximately 10⁸ Ohm × cm of the unmodified material to 10⁻³ Ohm × cm for the material containing 4.5 wt% of MWCNTs. the sample with 30% of filler exhibited the typical behavior of a conductive material, and it was demonstrated that it could be used as an in situ strain sensor. All these findings confirm the potential of the developed materials for advanced technological applications. Full article

High-speed Fused Filament Fabrication (FFF) printers are prone to nozzle vibration caused by moving-part inertia, frame flexibility, and modal coupling during high-acceleration motion, which can reduce deposition-trajectory stability. This study evaluates the dynamic adaptability of electronics layout and X-axis beam configurations for a CoreXY FFF printer under complete-machine boundary conditions. A finite element model including the frame, XY motion mechanism, print head, heated bed, and electronics was established. Modal and Y-direction harmonic response analyses were performed by first comparing rear-mounted and bottom-mounted electronics layouts and then by comparing three beam designs. With the baseline beam, both layouts had a first natural frequency of 83 Hz, whereas the rear-mounted layout increased the second- to sixth-order frequencies by 6.8%, 24.2%, 32.0%, 29.2%, and 15.3%. Under the rear-mounted layout, the three beams showed similar first six modal frequencies, but the perforated beam produced the lowest nozzle peak, with a full-band Y-direction response of 0.402 mm, which was 16.1% and 20.4% lower than those of the baseline and hollow square beams, respectively. This beam also had a mass of 41.98 g, which was 45.1% lower than that of the baseline beam. Therefore, rear-mounted electronics combined with a perforated beam was preferred within the current simulation. Full article

In the field of flexible electronics, traditional composite copper foils generally suffer from weak interfacial adhesion between the copper layer and polymer substrate, poor corrosion resistance, insufficient surface uniformity, and limited functional adaptability. To address these issues, Cu/Cu-CeO₂ composite coatings were deposited on polyimide (PI) substrates via PVD magnetron sputtering using argon as the working gas, aiming to enhance the comprehensive properties of composite copper foils, including interfacial bonding strength and corrosion resistance. Initially, pure Cu coatings were deposited on polyimide (PI), polyethylene terephthalate (PET), and polypropylene (PP) substrates. The deposition parameters were optimized through orthogonal and single-factor experiments, and the optimal process combination was determined as follows: PI substrate, sputtering time of 20 min, sputtering power of 60 W, and argon flow rate of 90 sccm, which achieved a balance between mechanical and electrical properties. Subsequently, comparative studies of Ar plasma treatment (100 s, 200 s, 300 s, and 400 s) and NaOH chemical etching (0 mol/L, 1 mol/L, 2 mol/L, and 3 mol/L) were conducted on the three polymer substrates. Comprehensive analyses of water contact angle, surface energy, bonding strength, and surface roughness demonstrated that the PI substrate treated with Ar plasma for 300 s exhibited superior overall performance, with a water contact angle of 48.5°, surface energy of 61.78 × 10⁻³ J/m², bonding strength of 4.56 N, and surface roughness of 0.89 μm. On this basis, the performance of pure Cu coatings and Cu/Cu-CeO₂ composite coatings prepared under different CeO₂ sputtering powers (20 W, 30 W, 40 W, and 50 W) was further investigated. Combined analyses of SEM, EDS, and XPS characterizations, together with bonding strength, resistivity, electrochemical impedance spectroscopy, polarization curves, and corrosion morphology tests, revealed that the Cu/Cu-CeO₂ composite coating prepared at a sputtering power of 50 W exhibited superior overall performance in terms of interfacial bonding strength and corrosion resistance. Full article

Deep learning (DL) models have achieved remarkable progress in natural language processing, computer vision, content generation, and edge intelligence; however, their rapidly increasing computational complexity, memory demand, and deployment diversity pose significant challenges for practical implementation. Field-programmable gate arrays (FPGAs) provide customized low-precision computation, spatial dataflow, on-chip data reuse, reconfigurability, and rich I/O capabilities, making them an important platform for DL inference. This paper presents a systematic review of FPGA-based DL deployment from a cross-layer perspective spanning model, compiler, architecture, runtime, and electronic design automation (EDA). Following a PRISMA-guided evidence synthesis protocol, this review analyzes DL workload characteristics, FPGA architectural optimizations, deployment toolflows, and physical implementation challenges. A unified taxonomy is proposed along the specialization–programmability continuum, including model-fixed accelerators, generator-based accelerators, template-configurable accelerators, and ISA-programmable overlays. These approaches are compared according to hardware regeneration requirements, model adaptability, operator coverage, compilation cost, and deployment flexibility. Furthermore, emerging workloads, including vision Transformers, graph neural networks, large language models, and multimodal models, are analyzed from the perspectives of computation, memory behavior, and runtime coordination. The review shows that FPGA deployment efficiency increasingly depends on memory capacity, mutable state management, operator support, and end-to-end compilation capability rather than peak multiply–accumulate throughput alone. Based on the analysis of 70 primary FPGA implementation studies, this paper highlights that reliable cross-study comparison requires careful consideration of model configuration, precision, execution phase, batch size, memory residency, FPGA platform, and evidence maturity. For multimodal generative models, the current evidence remains limited, with no identified end-to-end FPGA-based vision–language model implementation in the reviewed corpus. This review provides a systematic perspective for future FPGA-based DL deployment research, emphasizing cross-layer optimization, physically aware compilation, extensible accelerator architectures, and practical deployment efficiency. Full article

Group III-nitride semiconductors (GaN, AlN, InN) serve as foundational materials for modern optoelectronics, high-frequency microelectronics, and next-generation energy harvesting devices. However, traditional high-temperature epitaxy (>700 °C) introduces severe thermal stress, high dislocation densities, and fundamental incompatibility with flexible substrates or CMOS back-end-of-line (BEOL) processes. Plasma-enhanced atomic layer deposition (PEALD) provides a disruptive, ultra-low thermal budget (<300 °C) pathway for atomic-scale precision growth and conformal coating. This review systematically summarizes recent frontiers in PEALD-synthesized Group III-nitrides and 2D/3D polar heterostructures. First, we dissect the microscopic nucleation kinetics, surface bond reconstruction, and impurity suppression mechanisms across diverse substrates, including Si, sapphire, quartz, metals, and flexible polymers. Next, we highlight 2D template-assisted van der Waals epitaxy on graphene and MoS2, and elucidate polarization-driven dipole interactions and band alignment engineering at 2D/3D polar interfaces (e.g., α-In₂Se₃, Janus MoSSe). Furthermore, we comprehensively discuss innovative applications in advanced photovoltaics (as electron transport and passivation layers in perovskite and quantum dot-sensitized solar cells), silicon-based microcavity lasers, high-electron-mobility transistors (HEMTs), and flexible multimodal sensors. Finally, key technological challenges—including the low-thermal-budget paradox, wafer-scale uniformity, and deposition throughput—are addressed alongside future perspectives in area-selective ALD and neuromorphic computing, presenting a cohesive blueprint from underlying physics to macroscopic system integration. Full article

This study presents the design, fabrication, and experimental validation of the Hermes reusable model rocket platform integrating additive manufacturing, onboard sensing, and telemetry capabilities for low-cost aerospace experimentation. The rocket was manufactured using modular Polyethylene Terephthalate Glycol (PETG) components produced through fused filament fabrication to achieve a lightweight and structurally robust configuration suitable for repeated flight operations. A custom flight computer based on a Raspberry Pi Zero 2W was developed to acquire in-flight data from an inertial measurement unit, barometric pressure sensor, and Global Positioning System module, while an onboard camera enabled post-flight trajectory assessment. Aerodynamic performance and stability were evaluated using OpenRocket simulations, and propulsion was provided by a cluster of Klima D9-5 solid rocket motors. Four experimental flights were conducted to evaluate the integrated system architecture, assess telemetry and sensor performance, and compare experimental flight data with simulation predictions. The recorded measurements successfully captured the primary flight phases, including launch, ascent, apogee, descent, and recovery. The experimental results showed qualitative agreement with the simulated flight profiles; however, deviations in apogee altitude, acceleration, and flight duration were observed due to aerodynamic drag, environmental disturbances, motor-performance variability, and implementation-related limitations. The flight campaigns additionally identified practical challenges associated with wireless telemetry reliability, GPS signal acquisition, electronic protection, and parachute deployment, leading to iterative system improvements. From a sensing perspective, the flight campaigns demonstrate the operation and limitations of a low-cost embedded acquisition architecture under dynamic conditions, including the effects of sampling rate, sensor calibration, synchronization, wireless-link interruption, and local data preservation on the quality of the recorded flight measurements. The presented platform demonstrates the feasibility of combining low-cost additive manufacturing techniques with commercially available embedded electronics for reusable aerospace testing and educational applications. The proposed system further provides a flexible experimental framework for flight-data acquisition, simulation validation, and iterative development in academic and amateur rocketry research. Full article