Search Results (905)

This review investigates how process parameters and material choices influence the mechanical performance of parts produced by material extrusion additive manufacturing, with a particular focus on Material Extrusion (ME). Through a systematic bibliometric analysis of literature between 2015 and 2025, the study identifies key factors affecting mechanical strength, anisotropy, and structural reliability, including printing temperature, speed, orientation, layer thickness, and interlayer bonding. Emphasis is placed on emerging techniques such as 4D printing, fiber-reinforced composites, and novel monitoring methods like real-time vibration sensing and thermal imaging, which offer promising pathways to improve part performance and process stability. Three research questions guide the analysis: (1) how printing parameters affect micro- to macrostructure and failure behavior, (2) how optimization strategies enhance part quality, and (3) how material and process selection aligns with functional requirements. The review highlights both advances and persistent limitations in process control, material compatibility, and anisotropic strength. It concludes with a call for further integration of predictive modeling, hybrid material systems, and closed-loop process monitoring to unlock the full potential of additive manufacturing in high-performance engineering applications. Full article

The electric power equipment industry is rapidly advancing toward “informationization,” with the swift progression of intelligent sensing technology serving as a key driving force behind this transformation, thereby triggering significant changes in global electric power equipment. In this process, intelligent sensing has created an urgent demand for high-performance integrated power systems that feature compact size, lightweight design, long operational life, high reliability, high energy density, and low cost. However, the performance metrics of traditional power supplies have increasingly failed to meet the requirements of modern intelligent sensing, thereby significantly hindering the advancement of intelligent power equipment. Energy harvesting technology, characterized by its long operational lifespan, compact size, environmental sustainability, and self-sufficient operation, is capable of capturing renewable energy from ambient power sources and converting it into electrical energy to supply power to sensors. Due to these advantages, it has garnered significant attention in the field of power sensing. This paper presents a comprehensive review of the current state of development of energy harvesting technologies within the power environment. It outlines recent advancements in magnetic field energy harvesting, electric field energy harvesting, vibration energy harvesting, wind energy harvesting, and solar energy harvesting. Furthermore, it explores the integration of multiple physical mechanisms and hybrid energy sources aimed at enhancing self-powered applications in this domain. A comparative analysis of the advantages and limitations associated with each technology is also provided. Additionally, the paper discusses potential future directions for the development of energy harvesting technologies in the power environment. Full article

One of the major challenges in diagnosing various diseases, including neurological and neurodegenerative disorders, as well as carcinogenesis, is detecting unlabeled neurotransmitters. Surface-enhanced Raman spectroscopy (SERS) and surface-enhanced infrared spectroscopy (SEIRA) are promising methods for neurotransmitter biosensing and bioimaging. These methods are unique in that they are non-destructive and can identify molecular fingerprints. In this study, these methods were used to detect the following potent bradykinin (BK) antagonists: [D-Arg⁰,Hyp³,Thi⁵,D-Tic⁷,Oic⁸]BK, [D-Arg⁰,Hyp³,Thi⁵,D-Phe⁷,Thi⁸]BK, [D-Arg⁰,Hyp³,Igl⁵,D-Phe(5F)⁷,Oic⁸]BK, and [D-Arg⁰,Hyp³,Igl⁵,D-Igl⁷,Oic⁸]BK. The peptides were immobilized on a sensor surface consisting of silver (AgNPs) and gold (AuNPs) nanoparticles. These sensors have uniform particle sizes and small size distributions. Thanks to fast synthesis, easy handling, and reproducible results, these sensors enable routine testing. The vibrational structure of these peptides could not be determined using classical vibrational methods (Raman and IR) or surface-enhanced methods (SERS and SEIRA). This work presents the results of that research. Additionally, the SEIRA spectrum for BK or its analogs has not yet been published. This study presents research using SERS and SEIRA that shows that AgNP and AuNP sensors can detect the peptides under investigation. SERS is a more selective method than SEIRA because it allows for the differentiation of peptides based on the enhancement of certain bands in the SERS spectra. Furthermore, each peptide uniquely interacts with AuNPs, whereas all peptides bind to AgNPs via the C-terminus in different orientations. Consequently, the AuNP sensor is more selective than the AgNP sensor. Some bands were selected as markers for the sensing of specific peptides. Full article

This article presents the results of an experimental study focused on evaluating the potential to harvest electrical energy from vertical vibrations affecting a child car seat installed on an ISOFIX base with a support leg during real driving conditions. The objective was to measure vibration levels in the seat structure and assess the feasibility of converting this mechanical energy into electrical power. The study involved two child seat models, each tested under loads of 9 kg and 15 kg, while driving over smooth asphalt, damaged asphalt, and speed bumps. Acceleration data were collected at three key structural locations: the seat surface, the ISOFIX base, and the support leg. These measurements served as the basis for estimating the mechanical energy available and the resulting electrical output. Findings show that in poor road conditions, the system can generate enough energy to power a 10 µW sensor for more than 42 days. The results confirm the feasibility of using vibration energy harvesting to supply smart safety features such as presence detection, temperature monitoring, or posture sensing in child seats, without the need for batteries or a connection to the vehicle’s electrical system. Full article

The dynamic response in propane atmospheres at different voltages was investigated for samples made from powders of the semiconductor oxide CoSb₂O₆ synthesized using the microwave-assisted colloidal method. Powders of the compound calcined at 700 °C were studied with X-ray diffraction, confirming the CoSb₂O₆ crystalline phase. The microstructural characteristics of the oxide were analyzed using scanning and transmission electron microscopy (SEM/TEM), revealing a high abundance of nanorods, nanoplates, and irregular nanoparticles. These nanoparticles have an average size of ~21 nm. Using UV-Vis, absorption bands associated with the electronic transitions of the CoSb₂O₆’s characteristic bonds were identified, which yielded a bandgap value of ~1.8 eV. Raman spectroscopy identified vibrational bands corresponding to the oxide’s Sb–O and Co–O bonds. Dynamic sensing tests at 300 °C confirmed the material’s p-type semiconductor behavior, showing an increase in resistance upon exposure to propane. Critically, these tests revealed that the sensor’s baseline resistance and overall response are tunable by the applied voltage (1–12 V), with the highest sensitivity observed at the lowest voltages. This establishes a clear relationship between the electrical operating parameters and the sensing performance. The samples exhibited good operational stability, capacity, and efficiency, along with short response and recovery times. Extra-dry air (1500 cm³/min) was used as the carrier gas to stabilize the films’ surfaces during propane detection. These findings lead us to conclude that the CoSb₂O₆ could serve as an excellent gas detector. Full article

Reliable estimation of operational modal parameters is essential in structural health monitoring (SHM), particularly when these parameters serve as damage-sensitive features. Modern distributed monitoring systems, often employing digital MEMS accelerometers, must account for timing uncertainties across sensor networks. Clock irregularities can lead to non-deterministic sampling, introducing uncertainty in the identification of modal parameters. In this paper, the effects of timing variability throughout the network are propagated to the final modal quantities through a Monte-Carlo-based framework. The modal parameters are identified using the covariance-driven stochastic subspace identification (SSI-COV) algorithm. A finite element model of a steel cantilever beam serves as a test case, with timing irregularities modeled probabilistically to simulate variations in sensing node clock stability. The results demonstrate that clock variability at both intra-node and inter-node levels significantly influences mode shape estimation and introduces systematic biases in the identified natural frequencies and damping ratios. The confidence intervals are calculated, showing increased uncertainty with greater timing irregularity. Furthermore, the study examines how clock variability impacts damage detection, offering metrological insights into the limitations of distributed vibration-based SHM systems. Overall, the findings offer guidance for designing and deploying monitoring systems with independently timed nodes, aiming to enhance their reliability and robustness. Full article

In geophysical exploration, laser remote sensing detection of seismic waves based on wavefront sensors can be used for geological detection and geophysical exploration. However, due to the high sensitivity of the wavefront sensor, it is easy to be affected by the environmental light and vibration, resulting in random noise, which is difficult to predict, thus significantly reducing the quality of the vibration signal and the detection accuracy. In this paper, a large amount of data is collected through a single-point vibration detection experiment, and the relationship between amplitude and spot centroid offset is analyzed and calculated. The real noisy vibration signal is denoised and signal enhanced by using a BiLSTM denoising convolutional self-encoder (BiL-DCAE). The irregular and unpredictable noise generated by various complex noise mixing is successfully suppressed, and its impact on the vibration signal is reduced. The signal-to-noise ratio of the signal is increased by 13.90 dB on average, and the noise power is reduced by 95.93%, which greatly improves the detection accuracy. Full article

This study investigates the impact of oxygen-containing functional groups (COO-Li, CO-Li, and O-Li) on the electronic and optical properties of graphene, with a focus on hydrogen sensing applications. Using density functional theory (DFT) calculations, we evaluated the thermodynamic feasibility of the functionalization and hydrogen adsorption processes. The Gibbs free energy changes (ΔG) for the functionalization of pristine graphene were calculated as −1233, −1157, and −1119 atomic units (a.u.) for COO-Li, CO-Li, and O-Li, respectively. These negative values indicate that the functionalization processes are spontaneous (ΔG < 0), with COO-Li being the most thermodynamically favorable. Furthermore, hydrogen adsorption on the functionalized graphene surfaces also exhibited spontaneous behavior, with ΔG values of −1269, −1204, and −1175 a.u., respectively. These results confirm that both functionalization and subsequent hydrogen adsorption are energetically favorable, enhancing the potential of these materials for hydrogen sensing applications. Among the functional groups we simulated, COO-Li exhibited the largest surface area and volume, which were attributed to the high electronegativity and steric influence of the carboxylate moiety. Based on the previously described results, we analyzed the interaction of these functionalized graphene systems with molecular hydrogen. The adsorption of two H₂ molecules per system demonstrated favorable thermodynamics, with lithium atoms serving as active sites for external adsorption. The presence of lithium atoms significantly enhanced hydrogen affinity, suggesting strong potential for sensing applications. Further, electronic structure analysis revealed that all functionalized systems exhibit semiconducting behavior, with band gap values modulated by the nature of the functional group. FTIR (Fourier-Transform Infrared Spectroscopy) and Raman spectroscopy confirmed the presence of characteristic vibrational modes associated with Li-H interactions, particularly in the 659–500 cm⁻¹ range. These findings underscore the promise of lithium-functionalized graphene, especially with COO-Li, as a tunable platform for hydrogen detection, combining favorable thermodynamics, tailored electronic properties, and spectroscopic detectability. Full article

This study investigates the performance of Distributed Acoustic Sensing (DAS) for detecting gas pipeline leaks under controlled experimental conditions, using multiple fiber cable types deployed both internally and externally. A 21 m steel pipeline with a 1 m test section was configured to simulate leakage scenarios with varying leak sizes (¼”, ½”, ¾”, and 1”), orientations (top, side, bottom), and flow velocities (2–18 m/s). Experiments were conducted under two installation conditions: a supported pipeline mounted on tripods, and a buried pipeline laid on the ground and covered with sand. Four fiber deployment methods were tested: three internal cables of varying geometries and one externally mounted straight cable. DAS data were analyzed using both time-domain vibration intensity and frequency-domain spectral methods. The results demonstrate that leak detectability is influenced by multiple interacting factors, including flow rate, leak size and orientation, pipeline installation method, and fiber cable type and deployment approach. Internally deployed black and flat cables exhibited higher sensitivity to leak-induced vibrations, particularly at higher flow velocities, larger leak sizes, and for bottom-positioned leaks. The thick internal cable showed limited response due to its wireline-like construction. In contrast, the external straight cable responded selectively, with performance dependent on mechanical coupling. Overall, leakage detectability was reduced in the buried configuration due to damping effects. The novelty of this work lies in the successful detection of gas leaks using internally deployed fiber optic cables, which has not been demonstrated in previous studies. This deployment approach is practical for field applications, particularly for pipelines that cannot be inspected using conventional methods, such as unpiggable pipelines. Full article

Hydrogels exhibit significant promise for advanced flexible sensing applications owing to their intrinsic softness, biocompatibility, and customizable functionalities. Nevertheless, their limited mechanical strength poses a critical barrier to practical implementation. In this study, we engineered a mechanically robust alginate/chitosan (SA/CS) hydrogel reinforced with meta-aramid (PMIA) nanofibers. The resulting composite hydrogel achieves a tensile strength of 16.8 MPa, substantially exceeding the performance of conventional biomass-derived hydrogels. When employed as a flexible sensor, the hydrogel demonstrates exceptional pressure-sensing capabilities, featuring high sensitivity (178.41 MΩ/MPa below 5 kPa), rapid response kinetics (0.4–0.8 s), and sustained stability (>200 cycles). Leveraging these properties, we successfully monitored vocal cord vibrations and finger motion trajectories, highlighting their potential for biomechanical sensing applications. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

In the field of interferometric fiber-optic sensing, the phase-shifting technique is well known as a highly efficient method for retrieving the phase signal from the interference light intensity. The rectangular-pulse binary phase modulation (RPBPM) method is a typical phase-shifting method with the advantages of high efficiency, low complexity, and easy array multiplexing. Exploring the impact of the parameters on the performance is of great significance for guiding its application in practical systems. In this study, the influence of the sampling interval and modulation depth deviation involved in the method is analyzed in detail. Through a comparative simulation analysis with the traditional heterodyne and phase-generated carrier methods, the superiority of the RPBPM method is effectively validated. Meanwhile, an improved method based on the ellipse fitting of the Lissajous figure is proposed to compensate for the error and improve the signal-to-noise-and-distortion ratio (SINAD) from 26.3 dB to 37.1 dB in a specific experiment. Finally, the experimental results guided by the above method show excellent performance in a practical vibration system. Full article

Developing innovative, low-cost halochromic materials for diagnosing microbial contamination in wounds and burns can effectively facilitate tissue regeneration. Here, we combine the pH-sensing capability of highly colorful red cabbage anthocyanins (RCAs) with their healing potential within a unique cellulose polymer film that mimics the skin matrix. Biological activities of RCA extract in bacterial cellulose (BC) showed no cytotoxicity and skin-sensitizing potential to human cells at concentrations of RCAs similar to those released from BC/RCA dressings (4.0–40.0 µg/mL). A decrease in cell viability and apoptosis was observed in human cancer cells with RCAs. The invisible eye detection of the early color change signal from RCAs in response to pH alteration by bacteria was recorded with a smartphone application. The incorporation of RCAs into BC polymer has altered the morphology of its matrix, resulting in a denser cellulose microfibril network. The complete coincidence of the vibrational modes detected in the absorption spectra of the cellulose/RCA composite with the modes in RCAs most likely indicates that RCAs retain their structure in the BC matrix. Affordable, sensitive halochromic BC/RCA hydrogels can be recommended for online monitoring of microbial contamination, making them accessible to patients. Full article

Underground blasting vibrations are a critical factor influencing the stability of mine slopes. However, existing studies have yet to establish a quantitative relationship or clarify the underlying mechanisms linking blasting-induced vibrations and slope deformation. Taking the Shilu Iron Mine as a case study, this research develops a dynamic mechanical response model of slope stability that accounts for blasting loads. By integrating slope radar remote sensing data and applying the Pearson correlation coefficient, this study quantitatively evaluates—for the first time—the correlation between underground blasting activity and slope surface deformation. The results reveal that blasting vibrations are characterized by typical short-duration, high-amplitude pulse patterns, with horizontal shear stress identified as the primary trigger for slope shear failure. Both elevation and lithological conditions significantly influence the intensity of vibration responses: high-elevation areas and structurally loose rock masses exhibit greater dynamic sensitivity. A pronounced lag effect in slope deformation was observed following blasting, with cumulative displacements increasing by 10.13% and 34.06% at one and six hours post-blasting, respectively, showing a progressive intensification over time. Mechanistically, the impact of blasting on slope stability operates through three interrelated processes: abrupt perturbations in the stress environment, stress redistribution due to rock mass deformation, and the long-term accumulation of fatigue-induced damage. This integrated approach provides new insights into slope behavior under blasting disturbances and offers valuable guidance for slope stability assessment and hazard mitigation. Full article

Material extrusion additive manufacturing (MEAM) has emerged as a versatile and widely adopted 3D printing technology due to its cost-effectiveness and ability to process a diverse range of materials. However, achieving consistent part quality and repeatability remains a challenge, mainly due to variations in process parameters and material behavior during fabrication. In-situ monitoring and advanced process control systems have been increasingly integrated into MEAM to address these issues, enabling real-time detection of defects, optimization of printing conditions, reliability of fabricated parts, and enhanced control over mechanical properties. This review examines the state-of-the-art in-situ monitoring techniques, including thermal imaging, vibrational sensing, rheological monitoring, printhead positioning, acoustic sensing, image recognition, and optical scanning, and their integration with process control strategies, such as closed-loop feedback systems and machine learning algorithms. Key challenges, including sensor accuracy, data processing complexity, and scalability, are discussed alongside recent advancements and their implications for industrial applications. By synthesizing current research, this work highlights the critical role of in-situ monitoring and process control in advancing the reliability and precision of MEAM, paving the way for its broader adoption in high-performance manufacturing. Full article