Engineering Sciences

149 items

Chemical Communications Sep 15, 2026
This review organizes lithium-sulfur battery catalyst research around the fundamental chemistry of sulfur conversion and its rate-limiting steps, links catalyst modulation strategies to descriptors, groups descriptors into electronic, thermodynamic, and structural categories with their property-performance relationships, and points toward universal descriptors as well as the potential roles of in situ characterization, computational modeling, and artificial intelligence in descriptor construction and the design of highly active catalysts.
This review organizes lithium-sulfur battery catalyst research around the fundamental chemistry of sulfur conversion and its rate-limiting steps, links catalyst modulation strategies to descriptors, groups descriptors into electronic, thermodynamic, and structural categories with their property-performance relationships, and points toward universal descriptors as well as the potential roles of in situ characterization, computational modeling, and artificial intelligence in descriptor construction and the design of highly active catalysts.
Rational design of advanced electrocatalysts based on reactivity descriptors for high-performance lithium-sulfur batteries
This review organizes lithium-sulfur battery catalyst research around the fundamental chemistry of sulfur conversion and its rate-limiting steps, links catalyst modulation strategies to descriptors, groups descriptors into electronic, thermodynamic, and structural categories with their property-performance relationships, and points toward universal descriptors as well as the potential roles of in situ characterization, computational modeling, and artificial intelligence in descriptor construction and the design of highly active catalysts.
This review organizes lithium-sulfur battery catalyst research around the fundamental chemistry of sulfur conversion and its rate-limiting steps, links catalyst modulation strategies to descriptors, groups descriptors into electronic, thermodynamic, and structural categories with their property-performance relationships, and points toward universal descriptors as well as the potential roles of in situ characterization, computational modeling, and artificial intelligence in descriptor construction and the design of highly active catalysts.
Preprints.org Sep 15, 2026
This review systematically surveys state-of-the-art emergency rescue technologies for building fires and industrial thermal disasters, covering early fire detection integrating the Internet of Things (IoT), artificial intelligence (AI), and machine learning; advanced suppression technologies such as water mist, gaseous agents, and high-expansion foam; autonomous and semi-autonomous rescue robotics; UAVs equipped with thermal imaging; Building Information Modeling (BIM) and digital twins for emergency planning and evacuation simulation; PPE advancements including biometric wearables; and communication and situational-awareness systems, with special attention to industrial thermal hazards including petrochemical fires, hazardous material incidents, BLEVE events, and dust explosions, and conc
This review systematically surveys state-of-the-art emergency rescue technologies for building fires and industrial thermal disasters, covering early fire detection integrating the Internet of Things (IoT), artificial intelligence (AI), and machine learning; advanced suppression technologies such as water mist, gaseous agents, and high-expansion foam; autonomous and semi-autonomous rescue robotics; UAVs equipped with thermal imaging; Building Information Modeling (BIM) and digital twins for emergency planning and evacuation simulation; PPE advancements including biometric wearables; and communication and situational-awareness systems, with special attention to industrial thermal hazards including petrochemical fires, hazardous material incidents, BLEVE events, and dust explosions, and conc
Emergency Rescue Technology for Building Fire and Industrial Thermal Disaster: A Comprehensive Review
This review systematically surveys state-of-the-art emergency rescue technologies for building fires and industrial thermal disasters, covering early fire detection integrating the Internet of Things (IoT), artificial intelligence (AI), and machine learning; advanced suppression technologies such as water mist, gaseous agents, and high-expansion foam; autonomous and semi-autonomous rescue robotics; UAVs equipped with thermal imaging; Building Information Modeling (BIM) and digital twins for emergency planning and evacuation simulation; PPE advancements including biometric wearables; and communication and situational-awareness systems, with special attention to industrial thermal hazards including petrochemical fires, hazardous material incidents, BLEVE events, and dust explosions, and conc
This review systematically surveys state-of-the-art emergency rescue technologies for building fires and industrial thermal disasters, covering early fire detection integrating the Internet of Things (IoT), artificial intelligence (AI), and machine learning; advanced suppression technologies such as water mist, gaseous agents, and high-expansion foam; autonomous and semi-autonomous rescue robotics; UAVs equipped with thermal imaging; Building Information Modeling (BIM) and digital twins for emergency planning and evacuation simulation; PPE advancements including biometric wearables; and communication and situational-awareness systems, with special attention to industrial thermal hazards including petrochemical fires, hazardous material incidents, BLEVE events, and dust explosions, and conc
Nature News Sep 15, 2026
This Nature news report surveys the global hunt for natural geological hydrogen now underway in almost 30 countries: venture capitalists have invested nearly US$500 million since 2023, a 2024 analysis suggests Earth's subsurface probably holds tens of trillions of tonnes of the gas, and if even a small fraction can be recovered at a profit it could meet projected clean-energy needs for 200 years, yet nobody knows whether deposits large enough to match the hype exist, with results from several wells expected within months.
This Nature news report surveys the global hunt for natural geological hydrogen now underway in almost 30 countries: venture capitalists have invested nearly US$500 million since 2023, a 2024 analysis suggests Earth's subsurface probably holds tens of trillions of tonnes of the gas, and if even a small fraction can be recovered at a profit it could meet projected clean-energy needs for 200 years, yet nobody knows whether deposits large enough to match the hype exist, with results from several wells expected within months.
Can Underground Hydrogen Spark a Green-Energy Revolution? The Drilling Rush and an Unknown Resource
This Nature news report surveys the global hunt for natural geological hydrogen now underway in almost 30 countries: venture capitalists have invested nearly US$500 million since 2023, a 2024 analysis suggests Earth's subsurface probably holds tens of trillions of tonnes of the gas, and if even a small fraction can be recovered at a profit it could meet projected clean-energy needs for 200 years, yet nobody knows whether deposits large enough to match the hype exist, with results from several wells expected within months.
This Nature news report surveys the global hunt for natural geological hydrogen now underway in almost 30 countries: venture capitalists have invested nearly US$500 million since 2023, a 2024 analysis suggests Earth's subsurface probably holds tens of trillions of tonnes of the gas, and if even a small fraction can be recovered at a profit it could meet projected clean-energy needs for 200 years, yet nobody knows whether deposits large enough to match the hype exist, with results from several wells expected within months.
RSC Advances Sep 14, 2026
Using Capsicum annuum L. (red pepper) seed extract as a natural reducing and capping agent, this study green-synthesized Ag/MgO nanocomposites, systematically evaluated sunlight-driven diclofenac degradation across irradiation time, catalyst dosage, Ag loading, pH, initial DCF concentration, electrolyte type and concentration, and irradiation source, found that 10% Ag/MgO was most active, achieving 80% degradation of 10 mg/L diclofenac within 180 min with hydroxyl radicals identified as the dominant reactive species, and developed an EEFO-optimized DT_LSBoost model (R = 0.9999; RMSE = 1.2 × 10⁻³) with a MATLAB graphical interface for rapid prediction.
Using Capsicum annuum L. (red pepper) seed extract as a natural reducing and capping agent, this study green-synthesized Ag/MgO nanocomposites, systematically evaluated sunlight-driven diclofenac degradation across irradiation time, catalyst dosage, Ag loading, pH, initial DCF concentration, electrolyte type and concentration, and irradiation source, found that 10% Ag/MgO was most active, achieving 80% degradation of 10 mg/L diclofenac within 180 min with hydroxyl radicals identified as the dominant reactive species, and developed an EEFO-optimized DT_LSBoost model (R = 0.9999; RMSE = 1.2 × 10⁻³) with a MATLAB graphical interface for rapid prediction.
Green synthesis of Ag/MgO nanocomposites from red pepper seed waste for efficient sunlight-driven diclofenac degradation: experimental investigation and EEFO-optimized DT_LSBoost predictive modeling
Using Capsicum annuum L. (red pepper) seed extract as a natural reducing and capping agent, this study green-synthesized Ag/MgO nanocomposites, systematically evaluated sunlight-driven diclofenac degradation across irradiation time, catalyst dosage, Ag loading, pH, initial DCF concentration, electrolyte type and concentration, and irradiation source, found that 10% Ag/MgO was most active, achieving 80% degradation of 10 mg/L diclofenac within 180 min with hydroxyl radicals identified as the dominant reactive species, and developed an EEFO-optimized DT_LSBoost model (R = 0.9999; RMSE = 1.2 × 10⁻³) with a MATLAB graphical interface for rapid prediction.
Using Capsicum annuum L. (red pepper) seed extract as a natural reducing and capping agent, this study green-synthesized Ag/MgO nanocomposites, systematically evaluated sunlight-driven diclofenac degradation across irradiation time, catalyst dosage, Ag loading, pH, initial DCF concentration, electrolyte type and concentration, and irradiation source, found that 10% Ag/MgO was most active, achieving 80% degradation of 10 mg/L diclofenac within 180 min with hydroxyl radicals identified as the dominant reactive species, and developed an EEFO-optimized DT_LSBoost model (R = 0.9999; RMSE = 1.2 × 10⁻³) with a MATLAB graphical interface for rapid prediction.
Knowledge-Based Systems Sep 14, 2026
This work proposes RIM (Retrieval-In-Matching), which renders UAV-viewpoint references from Google 3D Tiles across locations, altitudes, and orientations, adapts SALAD in two stages with pose-near positives and geographically distant hard negatives and then re-ranks Top-K candidates by local geometric consistency, and further freezes the adapted DINOv2-B retriever while distilling a local-descriptor decoder from its token field and a shallow VGG19 detail stream so that one query-side DINOv2-B forward supports both SALAD retrieval and local description; evaluated zero-shot on the reconstructed EPFL Urbanscape and self-collected Chang'an Park datasets, both geographically disjoint from the training data, RIM outperforms ten retrieval baselines, improving Recall@1 over SALAD by 8.55/13.
This work proposes RIM (Retrieval-In-Matching), which renders UAV-viewpoint references from Google 3D Tiles across locations, altitudes, and orientations, adapts SALAD in two stages with pose-near positives and geographically distant hard negatives and then re-ranks Top-K candidates by local geometric consistency, and further freezes the adapted DINOv2-B retriever while distilling a local-descriptor decoder from its token field and a shallow VGG19 detail stream so that one query-side DINOv2-B forward supports both SALAD retrieval and local description; evaluated zero-shot on the reconstructed EPFL Urbanscape and self-collected Chang'an Park datasets, both geographically disjoint from the training data, RIM outperforms ten retrieval baselines, improving Recall@1 over SALAD by 8.55/13.
RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs
This work proposes RIM (Retrieval-In-Matching), which renders UAV-viewpoint references from Google 3D Tiles across locations, altitudes, and orientations, adapts SALAD in two stages with pose-near positives and geographically distant hard negatives and then re-ranks Top-K candidates by local geometric consistency, and further freezes the adapted DINOv2-B retriever while distilling a local-descriptor decoder from its token field and a shallow VGG19 detail stream so that one query-side DINOv2-B forward supports both SALAD retrieval and local description; evaluated zero-shot on the reconstructed EPFL Urbanscape and self-collected Chang'an Park datasets, both geographically disjoint from the training data, RIM outperforms ten retrieval baselines, improving Recall@1 over SALAD by 8.55/13.
This work proposes RIM (Retrieval-In-Matching), which renders UAV-viewpoint references from Google 3D Tiles across locations, altitudes, and orientations, adapts SALAD in two stages with pose-near positives and geographically distant hard negatives and then re-ranks Top-K candidates by local geometric consistency, and further freezes the adapted DINOv2-B retriever while distilling a local-descriptor decoder from its token field and a shallow VGG19 detail stream so that one query-side DINOv2-B forward supports both SALAD retrieval and local description; evaluated zero-shot on the reconstructed EPFL Urbanscape and self-collected Chang'an Park datasets, both geographically disjoint from the training data, RIM outperforms ten retrieval baselines, improving Recall@1 over SALAD by 8.55/13.
npj Computational Materials Sep 14, 2026
This work introduces a modular, large language model (LLM)-powered agent pipeline that automatically extracts and structures composition–process–property attributes of concrete materials from tables and text in scientific publications, achieving F1 scores up to 0.98 across 17 open and proprietary models and, within about one hour, extracting over 10,000 records from 278 papers screened from more than 27,000 publications; after postprocessing this yields the largest open laboratory database for blended cement concrete with nearly 9,000 high-quality records and over 100 attributes, and machine learning analyses indicate that large, diverse, information-rich datasets improve both in-distribution accuracy and out-of-distribution generalization to unseen materials systems.
This work introduces a modular, large language model (LLM)-powered agent pipeline that automatically extracts and structures composition–process–property attributes of concrete materials from tables and text in scientific publications, achieving F1 scores up to 0.98 across 17 open and proprietary models and, within about one hour, extracting over 10,000 records from 278 papers screened from more than 27,000 publications; after postprocessing this yields the largest open laboratory database for blended cement concrete with nearly 9,000 high-quality records and over 100 attributes, and machine learning analyses indicate that large, diverse, information-rich datasets improve both in-distribution accuracy and out-of-distribution generalization to unseen materials systems.
Large language model-enabled automated data extraction for concrete materials informatics
This work introduces a modular, large language model (LLM)-powered agent pipeline that automatically extracts and structures composition–process–property attributes of concrete materials from tables and text in scientific publications, achieving F1 scores up to 0.98 across 17 open and proprietary models and, within about one hour, extracting over 10,000 records from 278 papers screened from more than 27,000 publications; after postprocessing this yields the largest open laboratory database for blended cement concrete with nearly 9,000 high-quality records and over 100 attributes, and machine learning analyses indicate that large, diverse, information-rich datasets improve both in-distribution accuracy and out-of-distribution generalization to unseen materials systems.
This work introduces a modular, large language model (LLM)-powered agent pipeline that automatically extracts and structures composition–process–property attributes of concrete materials from tables and text in scientific publications, achieving F1 scores up to 0.98 across 17 open and proprietary models and, within about one hour, extracting over 10,000 records from 278 papers screened from more than 27,000 publications; after postprocessing this yields the largest open laboratory database for blended cement concrete with nearly 9,000 high-quality records and over 100 attributes, and machine learning analyses indicate that large, diverse, information-rich datasets improve both in-distribution accuracy and out-of-distribution generalization to unseen materials systems.
Discover Nano Sep 14, 2026
This study builds Ti3C2Tx MXene/n-type monocrystalline silicon heterojunction solar cells, exposes them to 50 MeV protons and 1 MeV electrons (electron fluence 10^12–10^15 particles/cm^2, proton 10^12–10^14 particles/cm^2), characterizes them with XRD, Raman, SEM/TEM, EBSD, APT and LAMMPS atomistic simulation, and couples a physics-informed digital twin with Random Forest Regression, reporting over 84% retention of initial power conversion efficiency after irradiation versus about 55% for conventional Si cells, with R^2 > 0.96 for predictions of VOC, JSC, FF and PCE.
This study builds Ti3C2Tx MXene/n-type monocrystalline silicon heterojunction solar cells, exposes them to 50 MeV protons and 1 MeV electrons (electron fluence 10^12–10^15 particles/cm^2, proton 10^12–10^14 particles/cm^2), characterizes them with XRD, Raman, SEM/TEM, EBSD, APT and LAMMPS atomistic simulation, and couples a physics-informed digital twin with Random Forest Regression, reporting over 84% retention of initial power conversion efficiency after irradiation versus about 55% for conventional Si cells, with R^2 > 0.96 for predictions of VOC, JSC, FF and PCE.
Predictive Modelling and Experimental Analysis of Radiation-Resistant MXene–Silicon Heterojunction Solar Cells
This study builds Ti3C2Tx MXene/n-type monocrystalline silicon heterojunction solar cells, exposes them to 50 MeV protons and 1 MeV electrons (electron fluence 10^12–10^15 particles/cm^2, proton 10^12–10^14 particles/cm^2), characterizes them with XRD, Raman, SEM/TEM, EBSD, APT and LAMMPS atomistic simulation, and couples a physics-informed digital twin with Random Forest Regression, reporting over 84% retention of initial power conversion efficiency after irradiation versus about 55% for conventional Si cells, with R^2 > 0.96 for predictions of VOC, JSC, FF and PCE.
This study builds Ti3C2Tx MXene/n-type monocrystalline silicon heterojunction solar cells, exposes them to 50 MeV protons and 1 MeV electrons (electron fluence 10^12–10^15 particles/cm^2, proton 10^12–10^14 particles/cm^2), characterizes them with XRD, Raman, SEM/TEM, EBSD, APT and LAMMPS atomistic simulation, and couples a physics-informed digital twin with Random Forest Regression, reporting over 84% retention of initial power conversion efficiency after irradiation versus about 55% for conventional Si cells, with R^2 > 0.96 for predictions of VOC, JSC, FF and PCE.
Nature News Sep 14, 2026
A proof-of-concept study reports that a single implanted array of 253 electrodes on the brain's sensorimotor cortex can capture neural signals related to both speech and body movement and use artificial intelligence to turn brain activity into on-screen text while driving a personalized animated avatar, decoding verbal and non-verbal communication simultaneously within seconds of the user's intent.
A proof-of-concept study reports that a single implanted array of 253 electrodes on the brain's sensorimotor cortex can capture neural signals related to both speech and body movement and use artificial intelligence to turn brain activity into on-screen text while driving a personalized animated avatar, decoding verbal and non-verbal communication simultaneously within seconds of the user's intent.
‘Multifunctional’ brain implant translates speech and gestures in real time
A proof-of-concept study reports that a single implanted array of 253 electrodes on the brain's sensorimotor cortex can capture neural signals related to both speech and body movement and use artificial intelligence to turn brain activity into on-screen text while driving a personalized animated avatar, decoding verbal and non-verbal communication simultaneously within seconds of the user's intent.
A proof-of-concept study reports that a single implanted array of 253 electrodes on the brain's sensorimotor cortex can capture neural signals related to both speech and body movement and use artificial intelligence to turn brain activity into on-screen text while driving a personalized animated avatar, decoding verbal and non-verbal communication simultaneously within seconds of the user's intent.
arXiv Sep 12, 2026
This work presents a language-guided terrain-adaptive neural model predictive control (MPC) framework for autonomous traversal of articulated tracked robots (ATRs) in contact-rich environments such as stairwells and cluttered building interiors: a terrain-conditioned neural kinematics model predicts short-horizon task-state increments from a local height sequence and recent trajectories, neural MPC plans with multi-objective costs and strict feasibility constraints, and a large language model (LLM) enables terrain adaptation by proposing bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks; the compiled predictor enables a full control cycle within 100 ms, and across three traversal tasks and a multi-he
This work presents a language-guided terrain-adaptive neural model predictive control (MPC) framework for autonomous traversal of articulated tracked robots (ATRs) in contact-rich environments such as stairwells and cluttered building interiors: a terrain-conditioned neural kinematics model predicts short-horizon task-state increments from a local height sequence and recent trajectories, neural MPC plans with multi-objective costs and strict feasibility constraints, and a large language model (LLM) enables terrain adaptation by proposing bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks; the compiled predictor enables a full control cycle within 100 ms, and across three traversal tasks and a multi-he
Language-Guided Terrain-Adaptive Neural MPC for Autonomous Traversal of Articulated Tracked Robots
This work presents a language-guided terrain-adaptive neural model predictive control (MPC) framework for autonomous traversal of articulated tracked robots (ATRs) in contact-rich environments such as stairwells and cluttered building interiors: a terrain-conditioned neural kinematics model predicts short-horizon task-state increments from a local height sequence and recent trajectories, neural MPC plans with multi-objective costs and strict feasibility constraints, and a large language model (LLM) enables terrain adaptation by proposing bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks; the compiled predictor enables a full control cycle within 100 ms, and across three traversal tasks and a multi-he
This work presents a language-guided terrain-adaptive neural model predictive control (MPC) framework for autonomous traversal of articulated tracked robots (ATRs) in contact-rich environments such as stairwells and cluttered building interiors: a terrain-conditioned neural kinematics model predicts short-horizon task-state increments from a local height sequence and recent trajectories, neural MPC plans with multi-objective costs and strict feasibility constraints, and a large language model (LLM) enables terrain adaptation by proposing bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks; the compiled predictor enables a full control cycle within 100 ms, and across three traversal tasks and a multi-he

All results are shown

Page 15 · showing 9