Search Results (181)

In logic device development, gate-all-around nanosheet field-effect transistors (GAA NSFETs) are widely regarded as the future mainstream architecture. Due to an innovative stacked-channel design, a novel process module of channel release has been introduced, posing significant challenges to device manufacturing. The channel release quality plays a decisive role in the device’s turn-on voltage and operating speed. Meanwhile, the complex interferences are undoubtedly brought by diverse structures and manufacturing thermal budgets of GAA NSFETs. Here, the non-plasma gas etching, which is not yet widely used in the current industry, is adopted for channel release. The influences of nanosheet width, spacing, and annealing conditions on the etching process are systematically studied. A SiGe/Si etching selectivity as high as 87 is achieved. With increasing channel width, a downward trend in the single-sided damage in the central region of Si nanosheets is shown. At >100% over-etching, the Si single-sided damage in structures with different channel spacing is controlled below 1 nm. The intensified diffusion of Ge elements in the SiGe layer and a gradual slowdown of the SiGe etching rate are caused by increasing the annealing temperature. The root mean square (RMS) value of the channel surface roughness is reduced from 0.087 to 0.069 nm by adding the *H radical pretreatment into the process. These findings provide valuable guidance for developing a channel release etching process with high selectivity, low damage, a stable process window, and low fabrication difficulty. Full article

Radon (²²²Rn) is a noble, radioactive gas and tends to be accumulated in poorly ventilated enclosed spaces. Mainly due to its radioactive daughters and the α-particles emitted, ²²²Rn poses a risk of cancer and therefore its concentration in air and water should be kept under certain reference levels. Several methods have been developed to accurately measure ²²²Rn concentration in water, using α, β or γ counting. A well-established, but not the only, method involves γ-spectroscopy using a High-Purity Germanium (HPGe) detector to identify the ²²²Rn decay isotopes ²¹⁴Pb and ²¹⁴Bi, assuming they are in secular equilibrium with ²²²Rn. This technique requires costly, bulky equipment due to the HPGe’s operation at −196 °C and the need for substantial shielding. The present study introduces a more affordable and compact device, utilizing CdZnTe (CZT) crystals, which provide exceptional energy resolution in the 300 to 600 keV range, with nearly eight times the Full Width at Half Maximum (FWHM) of HPGe. Four stacked CZT detectors, each containing a 0.5 cm³ crystal, were compared with measurements from an HPGe detector. Water samples were collected from boreholes and taps in a region where radon concentration in water ranged from 10 to 900 Bq/L. The results are promising for samples around 100 Bq/L, considering the potential advancements of the device with larger CZT detectors. Additionally, the method has the potential for in situ use due to its handheld capability. Full article

The increasing demand for sustainable, affordable smart city infrastructure has heightened the need for low-cost near-real-time water quality monitoring systems. In this study, we propose Water-QI, a low-cost Internet of Things (IoT)-based environmental monitoring platform that combines budget-friendly sensors with deep learning for water quality index (WQI) assessment and forecasting. The sensing platform measures five key physicochemical parameters, namely temperature, total dissolved solids (TDS), pH, turbidity, and electrical conductivity, enabling continuous multi-parameter monitoring in urban water environments. To model temporal variations in water quality under both cloud-based and edge-oriented deployment scenarios, we evaluate multiple gated recurrent unit (GRU) architectures with different widths and depths. Experiments are conducted at two temporal resolutions, hourly and minute-level, in order to examine the trade-off between predictive accuracy and edge computational latencies. In the hourly scenario, the single-layer GRU with 64 units achieved the best overall balance, reaching a validation RMSE of 0.0281 and a test $R^2$ of 0.9820, while deeper stacked GRU models degraded performance substantially. In the minute-resolution scenario, shallow wider GRU models produced the best results, with the single-layer GRU with 512 units attaining the lowest validation RMSE (0.025548) and the 256-unit variant achieving nearly identical accuracy with much lower inference cost. The results show that increasing the GRU model length can yield improvements at high temporal granularity, whereas increasing the GRU layer depth consistently harms convergence and generalization. Overall, the findings indicate that shallow GRU architectures provide the most practical solution for accurate, low-cost, and scalable water quality forecasting. In particular, the 64-unit GRU is the most suitable choice for hourly periodic interval operation, while the 256-unit GRU offers the best edge computational speed and accuracy trade-off for minute-level near-real-time inference on resource-constrained devices. Full article

The Axial Flux Permanent Magnet Synchronous Motor (AFPMSM) has gained significant attention as a core power source for next-generation industrial sectors, including electric vehicles, wind turbines, robot joints, and drone propulsion motors, due to its high power density from a short axial length and large radial dimensions. Despite these structural advantages, cogging torque caused by magnetic interaction between the stator teeth and permanent magnets remains a critical drawback, inducing noise and vibration. While conventional Soft Magnetic Composite (SMC) core methods facilitate 3D flux paths, they suffer from low magnetic permeability, insufficient mechanical strength, and manufacturing complexity. To address these issues, this study proposes a stepped structure model utilizing electrical steel sheets to effectively reduce cogging torque. This structure features radial stacking of identical electrical steel sheets with varying widths, where each layer’s center is incrementally shifted in the rotational direction. This configuration achieves an effect analogous to continuous skewing without specialized 3D machining. To validate the proposed design, 3D Finite Element Analysis (FEA) was conducted. Results demonstrate that the peak-to-peak cogging torque was reduced to approximately 86% of the conventional model’s value, while maintaining the back-EMF reduction rate within 5%. By presenting a novel skewing technique, this research provides a practical alternative for high-precision and high-power AFPMSM. Full article

High-temperature proton exchange membrane fuel cells (HT-PEMFCs) coupled with methanol reforming hold promise for distributed energy systems, yet channel hydrodynamics and geometry optimization remain underexplored. This study develops a 3D multiphysics model to investigate coupled behaviors in HT-PEMFCs fueled by methanol reformate. Results reveal bifurcation-induced Dean vortices have dual effects: they cause flow maldistribution (15–18% velocity deviation) and contribute 50% of inlet pressure loss, while generating a lateral pumping effect that enhances local mass transfer. A continuous parametric sweep of channel widths (0.9–1.9 mm) identifies a voltage-dependent performance crossover—narrower channels (1.3 mm) excel at high voltages by improving electronic conduction, whereas wider channels (1.5 mm) perform better at low voltages by mitigating mass transfer limitations. These findings provide quantitative design criteria for optimizing flow field geometry in HT-PEMFC stacks. Full article

To meet the demands for miniaturization, lightweight design, and high performance in modern electronic systems, advanced 3D TSV technology enables a substantial increase in storage capacity even within physically constrained form factors. This paper proposes a schematic design methodology and system-level integrated modeling approach for a four-layer stacked micro-module based on wafer-level packaging. By leveraging heterogeneous chip fan-out technology and TSV-based vertical stacking, the fabricated DDR3 micro-module achieves a compact footprint of 14 × 9 × 3.5 mm, a storage capacity of 4 GB, and a 64-bit bus width. Compared to conventional board-level mounting, the module reduces the footprint area by 95%. Following comprehensive multi-level testing, the micro-module fully complies with standard protocol requirements, enabling a paradigm shift in form factors for mobile computing devices while enhancing computational density and energy efficiency in data center server applications. Full article

The 795 nm wavelength corresponds to the D1 transition of rubidium atoms and is widely used in atomic optical pumping, atomic clocks, magnetometers, and precision spectroscopy. For compact free-space collimation, beam shaping, and efficient fiber coupling, edge-emitting semiconductor lasers with reduced fast-axis (vertical) divergence are highly desirable, yet low-divergence designs at 795 nm remain limited. Here, we propose and demonstrate low-divergence photonic-crystal epitaxy (LD–PC) for 795 nm edge-emitting lasers. By engineering a periodic n-side photonic-crystal stack to place the fundamental vertical mode near the photonic band edge, the vertical mode is expanded while maintaining effective modal discrimination. Narrow-ridge Fabry–Pérot lasers based on GaAsP/AlGaAs single-quantum-well epitaxy were fabricated and characterized. The optimized LD–PC device (3 μm ridge width, 1 mm cavity length) delivers 227 mW at 200 mA with a threshold current of 23 mA, a slope efficiency of 1.28 W/A, and a peak wall-plug efficiency of 55% under continuous-wave operation at 25 °C. The measured far-field divergences (FWHMs) are 7.16° and 18.83° in the lateral and vertical directions, respectively, corresponding to a reduction in the vertical divergence from >40° in the reference structure to <20° with LD–PC. These results validate photonic-crystal epitaxy as an effective route toward compact, high-performance, low-divergence 795 nm semiconductor laser sources for rubidium-based atomic systems. Full article

The core component to ensure the refined and safe operation of distribution network scheduling is 10 kV bus load probabilistic prediction. However, existing probabilistic prediction methods suffer from insufficient dynamic feature extraction and compromised prediction reliability caused by quantile crossing. To address these issues, this paper proposes a 10 kV bus load probabilistic prediction method integrating multi-value quantile regression (MQR) and a temporal fusion ensemble learning model (ELM). Firstly, a temporal fusion ensemble learning model is constructed, which integrates multiple temporal fusion network (TFN) sub-models through a stacking framework to parallel extract multi-dimensional temporal features of loads, effectively enhancing its feature capture capability for complex load data. Secondly, MQR is introduced as the core objective function to synchronously generate multi-quantile load forecasting results, comprehensively depicting the load probability distribution. Finally, a Listwise Maximum Likelihood Estimation (ListMLE) ranking constraint mechanism is embedded, which optimizes quantile ordering through monotonicity constraints, significantly reducing the degree of quantile crossing and improving the interpretability of forecasting results. The results show that the MQR-ELM algorithm achieves a Prediction Interval Coverage Probability of 94.624% (close to the nominal coverage rate of 95%), a Prediction Interval Averaged Width of 588.526, a Crossing Degree Index of only 0.0476, and a Continuous Ranked Probability Score as low as 84.931. All core indicators are significantly superior to those of the comparative algorithms. Full article

Efficient development of tight reservoirs in shallow-water delta-front environments is often constrained by misaligned horizontal well design and the underlying geological architecture. To address this, a quantitative optimization workflow is proposed, integrating 3D architectural characterization of single sandbodies with reservoir simulation. Using the Chang 7 Member of the Ordos Basin as a case study, three dominant sandbody types—isolated channels, vertically stacked channels, and mouth bars—were characterized in terms of geometry, stacking pattern, and internal permeability anisotropy. High-resolution geological models incorporating stratigraphic cyclicity and heterogeneity were constructed. Local grid refinement around wellbores and fracture networks was implemented to improve simulation fidelity. Sensitivity analyses identified optimal values for horizontal section length, fracture stage, and fracture half-length for each sandbody architecture. The results indicate that production response is highly sensitive to sandbody geometry and heterogeneity, with diminishing returns observed beyond critical design thresholds. Field validation with three horizontal wells confirmed that optimized parameter sets aligned with geological architecture resulted in significantly improved and more stable oil production. To support application in similar reservoirs, a dimensionless design chart was developed using ratios of horizontal well length to sandbody length (L_h/L_s) and fracture length to sandbody width (L_f/W_s). This empirical tool enables rapid pre-drill assessments and informs well planning strategies aligned with sandbody architecture. By emphasizing the integration of geological and engineering disciplines, the approach offers a scalable framework for optimizing horizontal well design in geologically complex tight formations. Full article

The use of aeolian sand as a fine aggregate in concrete production provides a sustainable pathway to valorize abundant aeolian resources while alleviating the global shortage of natural construction aggregates. However, the high ultrafine particle content of aeolian sand results in the formation of highly porous interfacial transition zones (ITZ) between sand particles and cement paste, which is the primary cause of the inherent brittleness and inferior mechanical performance of aeolian sand concrete. To overcome this critical limitation, an alkali-activated surface layer (ASL) was constructed on aeolian sand via 4 mol/L KOH activation. This process induced the surface micro-dissolution of minerals to create high-density active ion sites (specifically Ca²⁺, K⁺, Na⁺, and Fe³⁺). These sites facilitated the precise anchoring of carbon nanotubes (CNTs) through the chemical coordination of single-stranded deoxyribonucleic acid (ssDNA). The influence of the ASL and the ssDNA/CNTs nanocomposite on the ITZ was elucidated through macro-mechanical testing and multi-scale microstructural characterization. Experimental results demonstrated that compressive strength, flexural strength, and compressive energy dissipation increased by 48%, 67%, and 42%, respectively. Microstructurally, the modification promoted a pore refinement mechanism, reducing the proportion of harmful (pores > 0.1 μm) from 51% to 20% and narrowing the ITZ width from 20–40 μm to 10–15 μm (a 67% reduction). The observed performance enhancement is attributed to the synergistic effect of the ASL and ssDNA/CNTs, which transforms the inherently weak ITZ into a chemically reinforced interfacial phase via molecular-scale coordination bonding and optimized stacking of cement hydration products. Full article

Since the introduction of denoising diffusion probabilistic models (DDPM) in 2020, diffusion-based image generation has achieved remarkable quality but remains computationally demanding for resource-constrained environments. This survey systematically analyzes over 100 publications from 2020 to 2025, presenting a four-layer optimization stack that encompasses model architecture, controllable mechanisms, sampling algorithms, and model compression. We address the fundamental “quality–efficiency–control” trilemma through three research questions: (1) the architectural complexity gap between U-shaped network (UNet) and diffusion transformer (DiT) models, (2) the parameter overhead spectrum of control mechanisms from ControlNet (42%) to NanoControl (0.024%), and (3) the theoretical impact of quantization and bit-width reduction on information loss. Our analysis reveals that instant image generation is achievable through algorithmic innovations such as step distillation and architectural pruning, reducing the sampling steps from 50 to 4–8 (or even 1) and computational cost by over 90%. We utilize the floating point operations (FLOPs) efficiency ratio (FER) to highlight the discrepancy between theoretical FLOPs reduction and actual efficiency, pointing towards the need for system-level optimization. Key findings demonstrate that DiT architectures exhibit high computational density (FER > 1.6) and low-bit quantization such as 8-bit weight, and activation (W8A8) maintains an optimal balance between compression and quality (Fréchet inception distance degradation ΔFID < 1.0), and lightweight control mechanisms enable sophisticated image control with a negligible parameter overhead. This survey provides a comprehensive algorithmic optimization roadmap for practitioners targeting efficient on-device image generation. Full article

Large-format pouch cells enable higher pack-level energy density and simplified system architecture, yet they pose significant thermal challenges due to long internal conduction paths, pronounced spatial gradients, and limited access to core temperature. This work develops a high-fidelity electro-thermal model for large-format cells based on an equivalent circuit network that mirrors the physical assembly of tabs, welds, and electrode stacks. The model couples three-dimensional ohmic conduction in tabs, welds, and current collectors with node-level equivalent circuit models in the stack, and uses measurement-anchored parameters. The model is used to study thermally critical design factors for a 44 Ah pouch cell, including thermal management configurations, tab width, tab thickness, and tab welding. Simulation results indicate that among four active cooling options, two-sided stack surface cooling achieves the lowest temperatures and the best uniformity, lowering the average temperature by about 11 °C relative to natural convection and reducing the temperature standard deviation to 1.43 °C. It also decreases the core maximum temperature by more than 9 °C, whereas other configurations provide only 4 to 5 °C core reductions. Changes to tab geometry and welding have minor effects except under one-sided tab cooling. Full article

Additive manufacturing has been adopted in several industries including the medical field to develop new personalised medical implants including tissue engineering scaffolds. Custom patient-specific scaffolds can be additively manufactured to speed up the wound healing process. The aim of this study was to design, fabricate, and evaluate a range of materials and scaffold architectures for 3D-printed wound dressings intended for soft tissue applications, such as skin repair. Multiple biocompatible polymers, including polylactic acid (PLA), polyvinyl alcohol (PVA), butenediol vinyl alcohol copolymer (BVOH), and polycaprolactone (PCL), were fabricated using a material extrusion additive manufacturing technique. Eight scaffolds, five with circular designs (knee meniscus angled (KMA), knee meniscus stacked (KMS), circle dense centre (CDC), circle dense edge (CDE), and circle no gradient (CNG)), and three square scaffolds (square dense centre (SDC), square dense edge (SDE), and square no gradient (SNG), with varying pore widths and gradient distributions) were designed using an open-source custom toolpath generator to enable precise control over scaffold architecture. An in vitro degradation study in phosphate-buffered saline demonstrated that PLA exhibited the greatest material stability, indicating minimal degradation under the tested conditions. In comparison, PVA showed improved performance relative to BVOH, as it was capable of absorbing a greater volume of exudate fluid and remained structurally intact for a longer duration, requiring up to 60 min to fully dissolve. Tensile testing of PLA scaffolds further revealed that designs with increased porosity towards the centre exhibited superior mechanical performance. The strongest scaffold design exhibited a Young’s modulus of 1060.67 ± 16.22 MPa and withstood a maximum tensile stress of 21.89 ± 0.81 MPa before fracture, while maintaining a porosity of approximately 52.37%. This demonstrates a favourable balance between mechanical strength and porosity that mimics key properties of engineered tissues such as the meniscus. Overall, these findings highlight the potential of 3D-printed, patient-specific scaffolds to enhance the effectiveness and customisation of tissue engineering treatments, such as meniscus repair, offering a promising approach for next-generation regenerative applications. Full article

Target-oriented grasping has become increasingly important in household and industrial environments, and deploying such systems on mobile robots is particularly challenging due to limited computational resources. To address these limitations, we present an efficient framework for real-time target-oriented grasping on resource-constrained platforms, supporting both click-based grasping for unknown objects and category-based grasping for known objects. To reduce model complexity while maintaining detection accuracy, YOLOv8 is compressed using a structured pruning method. For grasp pose generation, a pretrained GR-ConvNetv2 predicts candidate grasps, which are restricted to the target object using masks generated by MobileSAMv2. A geometry-based correction module then adjusts the position, angle, and width of the initial grasp poses to improve grasp accuracy. Finally, extensive experiments were carried out on the Cornell and Jacquard datasets, as well as in real-world single-object, cluttered, and stacked scenarios. The proposed framework achieves grasp success rates of 98.8% on the Cornell dataset and 95.8% on the Jacquard dataset, with over 90% success in real-world single-object and cluttered settings, while maintaining real-time performance of 67 ms and 75 ms per frame in the click-based and category-specified modes, respectively. These experiments demonstrate that the proposed framework achieves high grasping accuracy and robust performance, with a efficient design that enables deployment on mobile and resource-constrained robots. Full article

The growing density of Earth-orbiting objects demands improved Space Situational Awareness (SSA) to mitigate collision risks and sustain space operations. This study demonstrates a dual-purpose star tracker (ST) for SSA using data from the Resident Space Object Near-space Astrometric Reconnaissance (RSONAR) stratospheric balloon campaign under the 2022 Canadian Space Agency–Centre National d’Études Spatiales (CSA–CNES) STRATOS program. The low-cost optical payload—a wide-field monochromatic imager flown at 36 km altitude—acquired imagery subsequently used for post-processed attitude determination and Resident Space Object (RSO) detection. During stabilized pointing, over 27,000 images yielded sub-pixel astrometry and stable image quality (mean full-width-Half-maximum ≈ 388 arcsec). Photometric calibration to the Tycho-2 catalog achieved 0.37 mag root mean square (RMS) scatter, confirming radiometric uniformity. Apparent angular velocities of $7\times {10}^2$ to $8\times {10}^3$ arcsec ${\mathrm{s}}^{-1}$ corresponded to sunlit low-Earth-orbit (LEO) objects observed at 25°–35° phase angles. Covariance-weighted Mahalanobis correlation with two-line elements (TLEs) achieved sub-arcminute positional agreement. The Proximity Filtering and Tracking (PFT) algorithm identified 22,036 total RSO and 387 total streaks via image stacking. Results confirm that commercial off-the-shelf STs can serve as dual-use SSA payloads, and that stratospheric ballooning offers a viable alternative for optical SSA research. Full article