Search Results (3,698)

Accurate ocean wave sensing in polar and other low-temperature marine environments is of great significance for marine environmental observation, climate research, and navigation safety. However, conventional wave sensors rely on external power supplies and suffer from poor stability under low-temperature and high-salinity conditions, making long-term self-powered waves sensing a significant challenge. Herein, a highly stable composite eutectogel electrode is developed by integrating sodium lignosulfonate, Fe³⁺ crosslinking, Zn²⁺-carboxylate coordination interactions, and a choline chloride/urea deep eutectic solvent (DES). The DES effectively suppresses solvent crystallization and endows the gel with excellent low-temperature tolerance, while the synergistic effect of metal coordination and multiple non-covalent interactions constructs a robust ion-conducting network with enhanced structural stability. Furthermore, eutectogel-based composite electrode architecture is designed to improve electrical conductivity and charge collection efficiency, thereby enabling stable electrical output under harsh marine conditions. Based on the as-prepared eutectogel electrode, a self-powered solid–liquid triboelectric nanogenerator is fabricated for ocean wave-motion sensing. The device can detect the wave amplitude, with an accuracy of 0.2 cm, and sense the frequency of waves ranging from 0.2 Hz to 1.6 Hz. More importantly, the SL-TENG exhibits excellent environmental adaptability, operating reliably in 3.5 wt% simulated seawater and at 0 °C. The current retention ratio reaches approximately 91% at 0 °C, which is significantly higher than that of the hydrogel-based device (≈6%). The remarkably low-temperature and salt-tolerant performance originates from the stable ion-transport network and anti-freezing characteristics of the eutectogel electrode. This work provides an effective strategy for constructing environmentally resilient eutectogel-based triboelectric devices and offers a promising route toward self-powered wave sensing systems for long-term deployment in harsh marine environments. Full article

Understanding microclimatic variability at the land–atmosphere interface is essential for improving knowledge of atmospheric moisture dynamics in heterogeneous mountainous ecosystems. This study analyzes atmospheric relative humidity and soil moisture variability in an experimental Juglans neotropica Diels plantation located in the Ecuadorian Andes under real field conditions. An autonomous photovoltaic-powered monitoring system equipped with low-cost environmental sensors was deployed continuously for 60 days, generating more than 86,000 environmental measurements of atmospheric relative humidity above and below the canopy, together with soil moisture observations. The results revealed persistent vertical humidity stratification associated with canopy structure, characterized by systematically higher atmospheric humidity beneath the canopy compared to the upper atmospheric layer (ΔRH ≈ −36%). Strong intersensor coherence was observed between canopy levels (r = 0.8535), indicating stable temporal consistency in atmospheric variability patterns throughout the monitoring period. Soil moisture exhibited comparatively more stable temporal dynamics than atmospheric humidity, suggesting partial microclimatic decoupling between atmospheric and edaphic layers. The observed humidity gradients remained temporally stable during both daytime and nighttime conditions, supporting the interpretation of canopy-mediated atmospheric buffering processes within the plantation environment. From an ecological perspective, the results indicate that vegetation structure contributes to localized moisture retention, attenuation of short-term atmospheric fluctuations, and regulation of near-surface microclimatic conditions under heterogeneous Andean environmental conditions. Rather than focusing on instrumentation performance, the study provides empirical evidence of persistent canopy-related atmospheric regulation and moisture stratification in a native Andean forest species under continuous field monitoring conditions. These findings contribute to the understanding of land–atmosphere interactions, ecohydrological dynamics, and vegetation-mediated microclimatic regulation in mountainous ecosystems. Full article

Smart and sustainable mobility increasingly relies on distributed sensing, low-power communication technologies, and cloud-based ICT platforms. This article presents a comprehensive scientific analysis of the technological foundations of sensitized mobility, reviewing the state of the art in embedded sensing, distributed systems, and communication paradigms for future mobility challenges. The research project “MOST” and in particular its subgroup “Spoke 5” falls within this framework of sustainable and sensorized mobility, with numerous activities in data collection, analysis, and field experimentation. In order to allow data collection, retention and analysis, one of the challenges that we must address is the definition of an adequate ICT architecture. The core contribution of this work is the presentation of the MOST ICT architecture, designed as a containerized, scalable, and resilient infrastructure capable of integrating heterogeneous data coming from field-deployed systems. In addition, it discusses the primary research challenges encountered in the definition and development of the presented architecture by examining two representative case studies within the MOST-Spoke 5 research project: renewable energy charging stations for light electric vehicles and cyclists monitoring systems. Full article

The increasing demand for predictive maintenance in industrial environments requires edge-intelligent solutions in which latency, energy consumption, memory footprint, and data movement are strictly constrained. This paper presents a two-stage Edge AI architecture for rolling-bearing prognostics, designed for heterogeneous embedded deployment across two distinct processing levels: a smart programmable sensing unit for local low-complexity signal preprocessing and a low-power embedded multiprocessor for higher-level temporal prognostic reasoning. In the first stage, a tiny neural preprocessor processes high-frequency vibration measurements directly at the sensing level and produces a compact low-dimensional degradation representation, complemented by lightweight health-related physical features. In the second stage, an embedded temporal reasoning model analyzes sequences of these compressed representations to estimate bearing degradation and remaining useful life. In addition to numerical remaining useful life regression, the second stage includes diagnostic reasoning heads that produce maintenance-oriented categories from a restricted vocabulary, enabling interpretable diagnostic summaries without relying on cloud-based language models. The complete pipeline is designed for fully edge-resident operation and exported in deployment-compatible formats, with the objective of supporting practical integration into heterogeneous industrial edge platforms. The proposed framework is trained and evaluated on the PRONOSTIA bearing degradation dataset and positioned against representative recurrent and hybrid prognostic baselines from the literature. From the deployment viewpoint, the sensor-side stage requires 68.62 ms inference time with 2.07 KiB RAM and 1.35 KiB flash/weights, whereas the edge temporal stage runs in 49.2 ms with 90.68 MiB RAM and 67.71 MiB flash/weights. In terms of prognostic performance, the proposed model achieves an average normalized RMSE of 0.1616 and an average normalized MAE of 0.1311 on three held-out bearings, while the weakly supervised diagnostic heads reach accuracies of 0.9066 for degradation trend and 0.8872 for vibration evidence. Experimental results show that the proposed architecture provides an effective trade-off between compact sensor-side processing, temporal prognostic accuracy, monotonic degradation consistency, hardware deployability, and interpretable maintenance-oriented outputs, supporting the feasibility of fully edge-based predictive maintenance systems for rolling-bearing health monitoring. Full article

Ammonia is increasingly regarded as a carbon-free energy carrier for hard-to-electrify power sectors, including marine propulsion, heavy-duty transport, and distributed generation. Its direct use in internal combustion engines, however, is constrained by high ignition energy, low laminar flame speed, narrow flammability limits, slow low-temperature chemistry, and strong trade-offs among efficiency, nitrogen-containing emissions, and unburned ammonia slip. Hydrogen enrichment is one of the most effective routes for improving ammonia combustion reactivity, but it also introduces a multivariable control problem: hydrogen fraction, ammonia injection timing, injection mode, air-path dilution, ignition strategy, and aftertreatment operation are tightly coupled and strongly condition-dependent. This review synthesizes recent progress in ammonia–hydrogen and ammonia-based dual-fuel engine control from a control-oriented perspective. The discussion first summarizes application scenarios, nonlinear combustion-mode transitions, emission-formation pathways, and control-relevant metrics. It then compares actuator-level strategies, including ammonia injection timing and staging, port and direct injection, hydrogen energy-fraction scheduling, excess-air-ratio and EGR control, high-energy ignition, and turbulent jet ignition. Advanced optimization methods are further reviewed, with emphasis on model predictive control, control-oriented combustion and emission models, artificial-intelligence-based virtual sensors, and reinforcement-learning control. The analysis shows that the central challenge is no longer whether ammonia can burn in an engine, but how a controller can keep the system inside a narrow moving window bounded by misfire, knock, NO_x, N₂O, and NH₃ slip. Finally, future research priorities are proposed, including engine–aftertreatment co-optimization, physics-informed virtual sensing, digital-twin-assisted calibration, lightweight deployment on electronic control units, and robust control under fuel and aging uncertainty. Full article

This paper presents a microwave sensing platform based on a 32-element dipole array designed for near-field dielectric contrast mapping. The sensor utilizes an $8\times 8$ tightly coupled dipole array (TCDA) topology, where pairs of dipoles form unit cells that exploit electromagnetic coupling variations. A 32-way equal power divider network ensures uniform excitation across the aperture. Operating at 830 MHz, the dipole array exhibits high absorption (>90%), which enhances near-field intensity and sensitivity to surface perturbations. Experimental validation with dielectric samples, saline liquids of varying concentrations (${ϵ}_r\approx$ 70–78), and biological tissues demonstrates the array’s capability to map spatial variations in electromagnetic properties through rectified DC voltage shifts. When compared to a state-of-the-art multi-port Vector Network Analyzer (VNA) configurations, the proposed architecture offers a robust, low-complexity, proof-of-concept alternative by eliminating complex RF routing networks and multi-port switches. Full article

Wide-scale, spatio-temporal quantification of soil CO₂ efflux is essential for understanding terrestrial carbon dynamics, predicting climate change, and evaluating the carbon balance in managed and natural ecosystems. Rising global temperatures, changing land use patterns, and other activities aimed at boosting crop productivity have resulted in an increase in microbial activity, increasing the impact of soil on gas exchange. Therefore, it is important to measure ${\mathrm{CO}}_2$ gas exchange in situ, over wide areas and extended periods without manual intervention. However, current approaches such as remote sensing lacks sufficient spatial and depth resolution, while other direct measurements such as eddy covariance demand expensive infrastructure, limiting wide-scale deployment. In this work, we propose a low-cost, battery-operated ${\mathrm{CO}}_2$ sensing system that provides long-term and scalable monitoring of soil respiration and carbon flux, with the promise for high-resolution measurements. Our innovative design features a PVC-based gas chamber that periodically opens and closes to allow for gas exchange, and a sensor module with low-cost temperature, moisture, pressure, and ${\mathrm{CO}}_2$ sensors, with a low-power wireless LoRa network for real-time monitoring. Our system was rigorously validated through multiple outdoor deployments, over long periods to demonstrate its practicality. We observe that temperature, air pressure, and humidity trends show responsiveness to the environment. We also observe that ${\mathrm{CO}}_2$ emission flux rate vary significantly across different modules, underscoring the need for fine-grained spatial and temporal resolution in monitoring. Full article

This work presents the design evolution, fabrication, and characterisation of an additively manufactured ring-type thermal airflow sensor for in-pipe flow monitoring, developed employing exclusively Fused Deposition Modelling (FDM) additive manufacturing technology and a commercially available Carbon Nanotube (CNT)-enriched Biopolymer Polylactic Acid (PLA) composite filament. The design evolution proceeds through three progressive stages. In the first stage, a flat heater element is characterised through Constant-Current (CC) Joule heating experiments in order to derive the corresponding Temperature Coefficient of Resistance (TCR) and Thermal Resistance from the obtained experimental data. Consequently, a Finite Element Method (FEM) model implemented in COMSOL Multiphysics^® and calibrated with the extracted material parameters validates the experimental temperature–power relationship and predicts the convective cooling behaviour at various airflow velocities. In the second stage, the geometry is optimised by introducing a conductive trace with a reduced-cross-section central region; as a result, an equivalent thermal localisation is achieved at approximately 26% lower supplied power with respect to the initial heating element, enabled by the design freedom inherent in the FDM process. We should note that the specific sensing geometry can also be directly embedded into any 3D-printed structural component (e.g., a bracket or housing), enabling simultaneous local thermal heating and/or thermal monitoring together with structural functionality within a single printed part. In the third and final stage—the target device—a fully monolithic ring-type airflow sensor is directly integrated into a 3D-printed pipe segment during the printing process. Under constant-current excitation at 40 mA, the device exhibits a monotonically decreasing resistance with increasing airflow (ΔR ≈ 117 Ω over 0–4 m/s) due to convective cooling, while in a single flow-interruption cycle, approximately 79% of the flow-induced resistance change was recovered upon flow removal, with a residual offset of approximately 3% of the heated baseline. A coupled electrothermal FEM model of the device further supports the experimental response by comparing the simulated temperature rise with the values inferred from resistance measurements, while also clarifying the role of the effective internal convective cooling conditions imposed by the pipe geometry. Key features of the proposed device are low raw-consumables cost, fast on-site manufacturing employing a commercially available desktop 3D printer, monolithic construction free of wire-bonded interconnections, and simplicity, indicating its potential for flow monitoring and condition-based maintenance systems aboard vessels as well as in a wide range of industrial sectors. We should note that the present characterisation was performed under laboratory conditions employing a single prototype per design stage; the effects of humidity, salt exposure, vibration, temperature cycling, and material-batch variability remain to be assessed prior to shipboard deployment. Full article

Broadband direction-of-arrival (DOA) estimation using a single acoustic vector sensor (AVS) is an important problem in passive underwater source localization and underwater acoustic signal processing, especially for compact underwater platforms and passive acoustic monitoring applications. However, conventional grid-based sparse Bayesian learning (SBL) may suffer from grid mismatch when the true bearing lies between adjacent predefined grid points. Although a dense grid can reduce this mismatch, it increases computational cost and dictionary coherence. To address this problem, this paper proposes an acoustic-intensity-guided local grid-refinement SBL method, termed AI-LGR-SBL. The pressure and particle-velocity channels are first used to construct acoustic intensity information and detect candidate source regions. The coarse bearing results then guide target-related spectral peak selection during SBL iterations, and local grid refinement is performed only around the selected directions. Simulations involving single-source and two-source scenarios show that AI-LGR-SBL yields sharper spatial spectra and lower estimation errors than conventional grid-based SBL. Compared with basic SBL, AI-LGR-SBL reduces the RMSE by approximately 10% in the low-SNR region and by 4–7% at relatively high SNRs. Compared with globally dense-grid SBL, it reduces the average runtime by approximately 47.3% and 43.6% in the single-source and equal-power two-source scenarios, respectively. Lake-trial data further demonstrate clear bearing–time trajectories and effective sub-grid peak refinement, supporting the feasibility of the proposed method for broadband underwater DOA estimation and passive source localization using a single AVS. Full article

Wireless sensor networks have become essential tools for environmental monitoring, enabling distributed data acquisition in remote and dynamic environments. However, challenges related to communication reliability, energy efficiency, synchronization, and real-time data availability remain critical for long-term deployments. This work presents the design, implementation, and validation of a synchronous wireless sensor network tailored for environmental monitoring in the challenging conditions of an estuarine setting. The proposed architecture is based on Digi XBee SX 868 RF modules operating in DigiMesh mode with synchronized cyclic sleep, enabling coordinated measurements and low-power operation. The network comprises monitoring, repeater, and coordinator/gateway nodes, integrated with a web-based platform for real-time data visualization and management. A custom message exchange format was developed to support seamless transmission of monitoring information from sensors to the web server through the wireless mesh infrastructure. Field experiments were conducted to evaluate network coverage, synchronization performance, communication reliability, and energy consumption. The results demonstrated successful multi-hop communication over the estuarine area, stable synchronization among distributed nodes over extended periods, and energy savings through synchronized sleep operation. The developed web platform enabled reliable real-time data access and network management. The proposed system demonstrates the feasibility of deploying scalable, energy-efficient, and synchronized wireless sensor networks for long-term environmental monitoring in estuarine environments. Full article

This paper presents the development and experimental validation of a low-cost IoT-enabled turbidity monitoring platform intended for laboratory-scale bioprocess applications. The proposed system was designed as a modular turbidity acquisition subsystem that can be integrated into broader bioreactor automation platforms. The hardware architecture is based on an ESP8266 microcontroller, a TS-300B optical turbidity sensor, a resistive voltage divider for analog signal conditioning, an OLED display for local visualization, and a Google Sheets-based cloud logging solution. A blank-based relative Turbidity Index was defined in order to compensate for optical configuration and environmental variations. The embedded firmware implements multi-sample averaging, blank calibration, serial command control, local display updates, CSV logging, and optional cloud transmission through HTTP requests. The calibration procedure was performed using serial dilutions of a yeast suspension, and the obtained data were fitted using a nonlinear power-law model and a log-log representation. An additional comparison with OD600 reference measurements showed a monotonic relationship between the proposed Turbidity Index and conventional optical-density measurements. The system was further validated through a yeast-based monitoring experiment performed under consistent optical conditions. The results showed the capability of the platform to acquire, process, visualize, and store turbidity-related data over an extended interval. The proposed platform provides a practical, affordable, and reproducible solution for turbidity monitoring and IoT-based data acquisition in small-scale bioprocess applications. Full article

This paper describes the design and implementation of an automated irrigation control system that uses data collected by a wireless sensor network. Each sensor node, built on a custom-designed printed circuit board, includes sensors for light intensity, temperature, and a custom soil moisture sensor. Data is transmitted to a central control node via ESP-NOW, where it is processed and compared with configurable thresholds retrieved from Google Sheets over Wi-Fi. Irrigation is triggered automatically when conditions meet the remotely defined thresholds. A key contribution is the development and testing of a custom soil moisture sensor, with results compared to commercial models. The system supports low-power operation through deep sleep modes, enabling long-term field deployment. The novelty lies in the complete integration of hardware, software, and cloud-based control, providing a flexible and low-cost solution for precision agriculture. The system can be deployed in greenhouses or open fields and serves as a platform for future research in smart irrigation. The fundamental aspect is a very user-friendly solution for any farmer attributable to easy accommodation to the Google Sheets interface, no maintenance cost over the cloud account, and up to 45 days of battery life or a built-in alternative for solar power. Full article

To exploit ultra-low power logic and architectural design techniques for bio-sensors in the human body, wireless ultrasonic techniques have emerged as a strong candidate for intra-body power transmission, thanks to lower medium attenuation and higher permitted safe intensity levels. When sub-100 $\mathsf{\mu }$m dimensions are considered for the bio-sensor, most devices struggle to guarantee a suitable voltage and power for digital electronics due to additional scaling requirements. This study investigates three alternative piezoelectric micromachined ultrasonic transducers in aluminum nitride doped with scandium, as reported in the literature, operating in the range 1–10 MHz. Their respective advantages and limitations with regard to energy harvesting and signal transmission performance are analyzed. It is shown that devices with footprints of less than 100 × 100 $\mathsf{\mu }$m² can achieve voltage outputs of over 150 mV and average power greater than 100 nW. Full article

This study investigates the application of Artificial Neural Network (ANN), Long Short-Term Memory network (LSTM) as a representative of Recurrent Neural Network (RNN), and one-dimensional Convolutional Neural Network (1D-CNN) for time series prediction, demonstrated through a use case of low-cost gas sensor readings from transient signals. Despite the widespread use of these architectures in IoT forecasting applications, there is a lack of systematic comparative studies that evaluate their performance under identical experimental conditions, particularly in energy-constrained sensing scenarios. The primary objective is to evaluate the trade-offs between model accuracy, computational cost, and memory requirements under energy-efficient data acquisition scenarios. A comprehensive experimental analysis was conducted using 186 recorded transient samples, where all models were trained and evaluated under consistent preprocessing, identical data splits, and uniform hyperparameter settings. Performance was assessed using RMSE, MAE, $R^2$, training time, and model size as key evaluation metrics under varying input sequence lengths. The results show that the LSTM model achieved the highest accuracy, with an RMSE of 3.69%, $R^2$ of 0.85 and scaled MAE of 0.04, effectively capturing long-term temporal dependencies. The 1D-CNN exhibited a balanced compromise between accuracy and training efficiency, while the ANN provided the shortest training time but lower overall performance. Reducing the number of input readings from 186 to as few as 10–20 resulted in only a 2–4% increase in RMSE, with model size reductions of up to 50%, making such configurations particularly suitable for edge or embedded IoT devices. The findings demonstrate that artificial neural networks can maintain high prediction accuracy even under reduced data conditions, contributing to the development of low-power, resource-efficient sensing systems for intelligent and distributed IoT environments. Full article

We report a tabletop, versatile and lithography-free electrical contacting method for two-dimensional (2D) materials and van der Waals (vdW) heterostructures based on silver paint micromanipulation (SPMM). Operated under an ambient optical microscope, this additive, room-temperature approach circumvents the chemical solvents and high temperatures associated with conventional cleanroom processing used in electrode fabrication. We validate the efficacy of this strategy by fabricating devices based on high-quality mechanically exfoliated thin flakes on both rigid SiO₂/Si and flexible polycarbonate substrates. On rigid supports, SPMM-contact multilayer graphene devices exhibit linear Ohmic behavior with excellent environmental stability over multiple days and an ambipolar field effect. Gate-tunable multilayer graphene/few-layer MoS₂/multilayer graphene field-effect transistors demonstrate n-type gating with a two-terminal carrier mobility of $60 {\displaystyle \frac{{\mathrm{cm}}^2}{\mathrm{Vs}}}$ and time-resolved photoresponse under 660 nm and 415 nm illumination, with responsivities as high as 10 A/W at the lowest incident powers. The SPMM method can also be carried out on flexible polymeric substrates such as polycarbonate, which is notoriously difficult to work with in microfabrication. We demonstrate a flexible multilayer graphene device that functions as highly responsive piezoresistive strain sensors at low deformations with a gauge factor of 50. Finally, a fully integrated flexible vdW photodetector is tested up to 1.2% uniaxial tensile strain. Despite experiencing local micro-fracturing of the MoS₂ channel, the localized vdW junctions maintain robust charge collection, yielding photodetecting capabilities under tensile strain. This simple and cost-effective electrical contacting technique establishes a highly accessible platform for the rapid prototyping and mechanical testing of next-generation optoelectronics and flexible electronics based on 2D materials and vdW heterostructures. Full article