Pub Date : 2026-03-12DOI: 10.1016/j.eng.2026.02.024
Yi Tao, Yunfei Chen
Friction, the resistance arising during relative motion between surfaces, is among the oldest known but least fundamentally understood phenomena in science and engineering. It governs processes across an extraordinary range of scales from microelectromechanical systems <span><span>[1]</span></span> and automotive braking <span><span>[2]</span></span> to seismic faults <span><span>[3]</span></span>. However, despite its critical scientific importance, friction has long been confined to phenomenological laws that obscure its true physical origin. The empirical foundation of friction was laid in the late 15th century, when Leonardo da Vinci used the apparatus shown in <span><span>Fig. 1</span></span>(a) to demonstrate that the friction force is proportional to the normal load, independent of the apparent contact area, and roughly one-quarter of the normal load <span><span>[4]</span></span>. These insights remained unpublished until 1699, when Amontons independently rediscovered da Vinci’s findings and emphasized that friction is independent of the contact area, the sliding velocity, and the contacting materials <span><span>[5]</span></span>. A century later, Coulomb confirmed these findings using systematic experiments that ultimately gave rise to the Amontons–Coulomb law, which continues to inform engineering practice across a broad range of applications. However, Coulomb also concluded that other investigations showed Amonton’s law to be inexact, and that further detailed investigations were important <span><span>[5]</span></span>. While this simplification of the law did enable engineering practice for centuries, it fundamentally obscured the dissipative essence of friction, i.e., friction is not merely an isolated interfacial shear force but rather a macroscopic manifestation of internal energy dissipation within materials. To break through this cognitive bottleneck, a fundamental understanding requires moving beyond the empirical frameworks of da Vinci and Coulomb into nonequilibrium dynamics that govern frictional dissipation at the atomic scale, as illustrated in the inset of <span><span>Fig. 1</span></span>(a).<figure><span><img alt="" aria-describedby="cn0005" height="320" src="https://ars.els-cdn.com/content/image/1-s2.0-S2095809926001256-gr1.jpg"/><ol><li><span><span>Download: <span>Download high-res image (224KB)</span></span></span></li><li><span><span>Download: <span>Download full-size image</span></span></span></li></ol></span><span><span><p><span>Fig. 1</span>. (a) Schematic of Leonardo da Vinci’s friction measurement setup, with an inset that shows the atomic-scale interfacial contacts. (b) Schematic of the classical PT model in which a support drives a sphere through a spring to slide across a corrugated substrate, where <span><span style=""></span><span style="font-size: 90%; display: inline-block;" tabindex="0"><svg focusable="false" height="2.317ex" role="img" style="vertical-align: -0.582ex;" viewbox="0 -747.2 1075.5 997.6" wid
{"title":"The Phonon Origin of Friction: A New Paradigm","authors":"Yi Tao, Yunfei Chen","doi":"10.1016/j.eng.2026.02.024","DOIUrl":"https://doi.org/10.1016/j.eng.2026.02.024","url":null,"abstract":"Friction, the resistance arising during relative motion between surfaces, is among the oldest known but least fundamentally understood phenomena in science and engineering. It governs processes across an extraordinary range of scales from microelectromechanical systems <span><span>[1]</span></span> and automotive braking <span><span>[2]</span></span> to seismic faults <span><span>[3]</span></span>. However, despite its critical scientific importance, friction has long been confined to phenomenological laws that obscure its true physical origin. The empirical foundation of friction was laid in the late 15th century, when Leonardo da Vinci used the apparatus shown in <span><span>Fig. 1</span></span>(a) to demonstrate that the friction force is proportional to the normal load, independent of the apparent contact area, and roughly one-quarter of the normal load <span><span>[4]</span></span>. These insights remained unpublished until 1699, when Amontons independently rediscovered da Vinci’s findings and emphasized that friction is independent of the contact area, the sliding velocity, and the contacting materials <span><span>[5]</span></span>. A century later, Coulomb confirmed these findings using systematic experiments that ultimately gave rise to the Amontons–Coulomb law, which continues to inform engineering practice across a broad range of applications. However, Coulomb also concluded that other investigations showed Amonton’s law to be inexact, and that further detailed investigations were important <span><span>[5]</span></span>. While this simplification of the law did enable engineering practice for centuries, it fundamentally obscured the dissipative essence of friction, i.e., friction is not merely an isolated interfacial shear force but rather a macroscopic manifestation of internal energy dissipation within materials. To break through this cognitive bottleneck, a fundamental understanding requires moving beyond the empirical frameworks of da Vinci and Coulomb into nonequilibrium dynamics that govern frictional dissipation at the atomic scale, as illustrated in the inset of <span><span>Fig. 1</span></span>(a).<figure><span><img alt=\"\" aria-describedby=\"cn0005\" height=\"320\" src=\"https://ars.els-cdn.com/content/image/1-s2.0-S2095809926001256-gr1.jpg\"/><ol><li><span><span>Download: <span>Download high-res image (224KB)</span></span></span></li><li><span><span>Download: <span>Download full-size image</span></span></span></li></ol></span><span><span><p><span>Fig. 1</span>. (a) Schematic of Leonardo da Vinci’s friction measurement setup, with an inset that shows the atomic-scale interfacial contacts. (b) Schematic of the classical PT model in which a support drives a sphere through a spring to slide across a corrugated substrate, where <span><span style=\"\"></span><span style=\"font-size: 90%; display: inline-block;\" tabindex=\"0\"><svg focusable=\"false\" height=\"2.317ex\" role=\"img\" style=\"vertical-align: -0.582ex;\" viewbox=\"0 -747.2 1075.5 997.6\" wid","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"92 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147454710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Existing data-driven fire forecast systems often exhibit limitations in real-world emergency response scenarios, particularly with respect to efficient data reuse and vulnerability of sensor networks. This study proposes a smart agent that integrates an artificial intelligence (AI)-driven fire situational awareness engine with a large language model (LLM) to realize the diverse demands of emergency response in complex fire scenarios. First, a fire-resilient deep learning model based on ConvLSTM is developed to reconstruct building temperature fields using limited inputs from a partially failed temperature sensor network. The proposed architecture constructs spatiotemporal correlations between missing and survived sensor data, enabling the transformation of discrete temperature measurements into a continuous two-dimensional (2D) temperature contour. Subsequently, a smart agent powered by a domain-specific LLM is designed to enhance human–AI interaction during fire emergency response. A self-driven framework capable of automatically executing LLM-generated programs is established to deliver real-time, user-specific information to multiple stakeholders. Experimental results demonstrate that, compared with generic LLM-based responses, the proposed agent augmented with fire situational awareness can generate customized operational recommendations through dynamic interactions with the ConvLSTM-based fire model. This hybrid agent improves situational awareness and safety during fire emergencies, improves the resilience of fire services systems, and advances the practical implementation of AI-driven smart firefighting.
{"title":"Integrating Smart Fire Forecast with LLM-Powered Emergency Response","authors":"Weikang Xie, Yuxin Zhang, Tong Lu, Xianjia Huang, Jihao Shi, Xinyan Huang, Fu Xiao, Asif Usmani","doi":"10.1016/j.eng.2026.02.023","DOIUrl":"https://doi.org/10.1016/j.eng.2026.02.023","url":null,"abstract":"Existing data-driven fire forecast systems often exhibit limitations in real-world emergency response scenarios, particularly with respect to efficient data reuse and vulnerability of sensor networks. This study proposes a smart agent that integrates an artificial intelligence (AI)-driven fire situational awareness engine with a large language model (LLM) to realize the diverse demands of emergency response in complex fire scenarios. First, a fire-resilient deep learning model based on ConvLSTM is developed to reconstruct building temperature fields using limited inputs from a partially failed temperature sensor network. The proposed architecture constructs spatiotemporal correlations between missing and survived sensor data, enabling the transformation of discrete temperature measurements into a continuous two-dimensional (2D) temperature contour. Subsequently, a smart agent powered by a domain-specific LLM is designed to enhance human–AI interaction during fire emergency response. A self-driven framework capable of automatically executing LLM-generated programs is established to deliver real-time, user-specific information to multiple stakeholders. Experimental results demonstrate that, compared with generic LLM-based responses, the proposed agent augmented with fire situational awareness can generate customized operational recommendations through dynamic interactions with the ConvLSTM-based fire model. This hybrid agent improves situational awareness and safety during fire emergencies, improves the resilience of fire services systems, and advances the practical implementation of AI-driven smart firefighting.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"50 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wastewater-based surveillance (WBS) has emerged as an effective tool for monitoring infectious diseases. However, its broader application is often constrained by operational resources and data complexities. Herein, we developed an integrated framework that synergistically integrated WBS with theResearch Index, China’s leading online search query platform, to enhance early warning capability for infectious diseases using coronavirus disease 2019 (COVID-19) as a case study. A total of 1164 influent wastewater samples were collected from 12 wastewater treatment plants in Nanning, China, over a one-year period (February 2023–January 2024), and RNA was quantified using reverse transcription quantitative polymerase chain reaction (RT-qPCR). The 7-day flow-weighted moving average concentration (FWMAC) was calculated and evaluated in relation to 16 population surveillance indicators and 126 Baidu search terms. Remarkably, the 7-day FWMAC preceded clinical indicators by 1–7 days and demonstrated strong correlations with multiple epidemiological metrics, including reported cases (the coefficient of determination (R2) = 0.92), diagnosed cases in fever clinics (R2 = 0.72), positive diagnoses in fever clinics (R2 = 0.86), and hospitalizations (R2 = 0.78). Distributed lag nonlinear models were employed to define actionable and clinically relevant risk thresholds. We then identified three key Baidu search terms (“second positive,” “four stages of COVID-19 clinical progression,” and “ibuprofen”). Their integrated search index improved model performance (with R2 for diagnosed cases in fever clinics and hospitalizations increasing from 0.72 to 0.78 and 0.78 to 0.82, respectively). External validation across three additional major Chinese cities (Changsha, Xiamen, and Nanjing) further confirmed the model’s robustness and generalizability, yielding R2 improvements of 36.4%, 30.0%, and 4.9% in random forest models, respectively. This integrative “wastewater-digital” framework represents a transformative and cost-effective early warning strategy for proactive public health responses.
{"title":"Integrating Urban Wastewater Surveillance and Internet Search Behavior to Strengthen Early Warning for Infectious Diseases","authors":"Fu-Chang Deng, Hong Xu, Song-Zhe Fu, Qiao Yao, Jian-Qiu Qin, Cheng Yang, Yan-Feng Yao, Pu Li, Wei-Ying Tian, Xiao-Lei Wang, Ling-Shuang Lv, Xin Xia, Xia-Lu Lin, Rong-Qiu Zhang, Zhi-Nan Guo, Li-Lin Xiong, Shi-Fu Peng, Zhen Ding, Cao Chen, Yu Wang, Xiao-Ming Shi","doi":"10.1016/j.eng.2026.02.022","DOIUrl":"https://doi.org/10.1016/j.eng.2026.02.022","url":null,"abstract":"Wastewater-based surveillance (WBS) has emerged as an effective tool for monitoring infectious diseases. However, its broader application is often constrained by operational resources and data complexities. Herein, we developed an integrated framework that synergistically integrated WBS with theResearch Index, China’s leading online search query platform, to enhance early warning capability for infectious diseases using coronavirus disease 2019 (COVID-19) as a case study. A total of 1164 influent wastewater samples were collected from 12 wastewater treatment plants in Nanning, China, over a one-year period (February 2023–January 2024), and RNA was quantified using reverse transcription quantitative polymerase chain reaction (RT-qPCR). The 7-day flow-weighted moving average concentration (FWMAC) was calculated and evaluated in relation to 16 population surveillance indicators and 126 Baidu search terms. Remarkably, the 7-day FWMAC preceded clinical indicators by 1–7 days and demonstrated strong correlations with multiple epidemiological metrics, including reported cases (the coefficient of determination (<em>R</em><sup>2</sup>) = 0.92), diagnosed cases in fever clinics (<em>R</em><sup>2</sup> = 0.72), positive diagnoses in fever clinics (<em>R</em><sup>2</sup> = 0.86), and hospitalizations (<em>R</em><sup>2</sup> = 0.78). Distributed lag nonlinear models were employed to define actionable and clinically relevant risk thresholds. We then identified three key Baidu search terms (“second positive,” “four stages of COVID-19 clinical progression,” and “ibuprofen”). Their integrated search index improved model performance (with <em>R</em><sup>2</sup> for diagnosed cases in fever clinics and hospitalizations increasing from 0.72 to 0.78 and 0.78 to 0.82, respectively). External validation across three additional major Chinese cities (Changsha, Xiamen, and Nanjing) further confirmed the model’s robustness and generalizability, yielding <em>R</em><sup>2</sup> improvements of 36.4%, 30.0%, and 4.9% in random forest models, respectively. This integrative “wastewater-digital” framework represents a transformative and cost-effective early warning strategy for proactive public health responses.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"199 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-07DOI: 10.1016/j.eng.2026.01.027
Ding Yuan, Zhizhou Zhang, Wei Guo, Chao Wei, Xiaojing Sun, Jiahua Wang, Zeng Zhang, Junyao Zhang, Dongxu Cheng, Zhu Liu, Paul Mativeng, Lin Li
A new Hf-Zr-C-based metal ceramic composite capable of transforming into a ceramic at high temperatures was developed using deep learning (DL)–assisted materials design, with screening performed across approximately 20 million compositions. Components were directly fabricated from mixed powders by laser powder bed fusion (LPBF). The as-fabricated material exhibited high fracture toughness (6.32 MPa·m1/2), low thermal conductivity (6.34 W·m–1·K–1), and good room-temperature machinability, properties not achievable in traditional carbides. At elevated temperatures, the material transformed into a single-phase ceramic through solid-phase diffusion, with a measured melting point of (4181 ± 85) K. This study demonstrates a viable route for the design and direct additive manufacturing of refractory ceramic components from multi-powder systems.
{"title":"Deep Learning–Assisted Material Design and Laser Direct Additive Manufacturing of a Metal Ceramic Composite that Transforms into Ceramic at High Temperatures","authors":"Ding Yuan, Zhizhou Zhang, Wei Guo, Chao Wei, Xiaojing Sun, Jiahua Wang, Zeng Zhang, Junyao Zhang, Dongxu Cheng, Zhu Liu, Paul Mativeng, Lin Li","doi":"10.1016/j.eng.2026.01.027","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.027","url":null,"abstract":"A new Hf-Zr-C-based metal ceramic composite capable of transforming into a ceramic at high temperatures was developed using deep learning (DL)–assisted materials design, with screening performed across approximately 20 million compositions. Components were directly fabricated from mixed powders by laser powder bed fusion (LPBF). The as-fabricated material exhibited high fracture toughness (6.32 MPa·m<sup>1/2</sup>), low thermal conductivity (6.34 W·m<sup>–1</sup>·K<sup>–1</sup>), and good room-temperature machinability, properties not achievable in traditional carbides. At elevated temperatures, the material transformed into a single-phase ceramic through solid-phase diffusion, with a measured melting point of (4181 ± 85) K. This study demonstrates a viable route for the design and direct additive manufacturing of refractory ceramic components from multi-powder systems.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"6 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147368173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Road ponding presents a substantial threat to vehicular safety, particularly in foggy conditions where reliable detection continues to be a major challenge for advanced driver assistance systems (ADASs). To address this issue, we propose an aggregation-broadcast-coupling dynamic wavelet network (ABCDWaveNet), a novel deep learning framework specifically designed to achieve robust ponding detection in fog-affected environments. The central architecture of ABCDWaveNet improves detection performance by utilizing dynamic convolution for adaptive feature extraction under reduced visibility, together with a wavelet-based module that improves feature representation across both spatial and frequency domains, thereby effectively alleviating fog-related interference. In addition, ABCDWaveNet incorporates multi-scale structural and contextual information and employs an adaptive attention coupling gate to dynamically integrate global and local features, leading to improved detection accuracy. For realistic evaluations under compounded adverse weather conditions, we introduce the Foggy Low-Light Puddle dataset. Comprehensive experiments confirmed that ABCDWaveNet attained state-of-the-art results, with notable intersection over union gains of 3.51%, 1.75%, and 1.03% on the Foggy-Puddle, Puddle-1000, and Foggy Low-Light Puddle datasets, respectively. Furthermore, with an inference speed (FPS) of 25.48 on the NVIDIA Jetson AGX Orin, the proposed framework demonstrates strong suitability for development in ADAS applications. These results highlight the effectiveness of ABCDWaveNet, presenting valuable advancements for proactive road safety under challenging weather conditions.
{"title":"ABCDWaveNet: Advancing Robust Road Ponding Detection in Fog Through Dynamic Frequency-Spatial Synergy","authors":"Ronghui Zhang, Dakang Lyu, Tengfei Li, Yunfan Wu, Ujjal Manandhar, Benfei Wang, Junzhou Chen, Bolin Gao, Danwei Wang, Yiqiu Tan","doi":"10.1016/j.eng.2026.01.026","DOIUrl":"https://doi.org/10.1016/j.eng.2026.01.026","url":null,"abstract":"Road ponding presents a substantial threat to vehicular safety, particularly in foggy conditions where reliable detection continues to be a major challenge for advanced driver assistance systems (ADASs). To address this issue, we propose an aggregation-broadcast-coupling dynamic wavelet network (ABCDWaveNet), a novel deep learning framework specifically designed to achieve robust ponding detection in fog-affected environments. The central architecture of ABCDWaveNet improves detection performance by utilizing dynamic convolution for adaptive feature extraction under reduced visibility, together with a wavelet-based module that improves feature representation across both spatial and frequency domains, thereby effectively alleviating fog-related interference. In addition, ABCDWaveNet incorporates multi-scale structural and contextual information and employs an adaptive attention coupling gate to dynamically integrate global and local features, leading to improved detection accuracy. For realistic evaluations under compounded adverse weather conditions, we introduce the Foggy Low-Light Puddle dataset. Comprehensive experiments confirmed that ABCDWaveNet attained state-of-the-art results, with notable intersection over union gains of 3.51%, 1.75%, and 1.03% on the Foggy-Puddle, Puddle-1000, and Foggy Low-Light Puddle datasets, respectively. Furthermore, with an inference speed (FPS) of 25.48 on the NVIDIA Jetson AGX Orin, the proposed framework demonstrates strong suitability for development in ADAS applications. These results highlight the effectiveness of ABCDWaveNet, presenting valuable advancements for proactive road safety under challenging weather conditions.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"4 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147368208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-06DOI: 10.1016/j.eng.2026.02.021
Chiu Shek Wong, Zaixing Wang, Shuvra Saha, Haoyu Zhao, Yashan Lin, Song Cheng, Jie Mei, Wing Wa Chan, Shu Chuen Ip, Junkui Mao, Ka Wai Eric Cheng, Molly Meng-Jung Li
The aviation sector faces growing pressure to reduce carbon emissions, and electric propulsion systems (EPS) based on proton exchange membrane fuel cells (PEMFCs) provide a promising path toward sustainable, zero-carbon aviation. However, challenges related to hydrogen storage and transport have hindered the practical implementation of such systems. Ammonia (NH3), with high energy density, convenient storage and transport, and carbon neutrality, has emerged as an attractive hydrogen carrier. This study proposes and experimentally validates a compact NH3 cracking power generation system tailored for EPS through laboratory-scale exploration, engineering-scale validation, and system-level evaluation. The system delivers a maximum power output of 30 kW and comprises a custom-designed multifunctional NH3 cracking reactor with integrated heat recovery, a temperature swing adsorption (TSA) purification unit, and PEMFC stacks. To meet practical application needs, this study screens and optimizes a commercially available 1% Ru–Ni/Al2O3 catalyst, achieving over 99% NH3 conversion under realistic conditions. The TSA unit reduces NH3 concentration to below the detection limit, ensuring stable PEMFC performance with a single-stack maximum power output of 5.3 kW. Simulation results further show that the multi-stage thermal management increases the propulsion usable net electrical efficiency to 20.52%, and further raises the overall energy efficiency to 28.33% when the low-grade recovered heat is assumed fully usable. The optimized system achieves a gravimetric energy density of 692.7 W·h·kg−1 and a hydrogen storage capacity of 6.7 wt% when equipped with five NH3 tanks, each containing 22.7 kg of NH3. This work demonstrates an NH3-powered PEMFC EPS for aviation, offering both experimental validation and theoretical guidance for NH3-fueled propulsion technologies. The study provides system-level insights into design, integration, and performance optimization, supporting the future development of electrified aviation and related zero–carbon distributed energy systems.
{"title":"Experimental Exploration of An Ammonia Cracking Power Generation System for Electric Aircraft Propulsion","authors":"Chiu Shek Wong, Zaixing Wang, Shuvra Saha, Haoyu Zhao, Yashan Lin, Song Cheng, Jie Mei, Wing Wa Chan, Shu Chuen Ip, Junkui Mao, Ka Wai Eric Cheng, Molly Meng-Jung Li","doi":"10.1016/j.eng.2026.02.021","DOIUrl":"https://doi.org/10.1016/j.eng.2026.02.021","url":null,"abstract":"The aviation sector faces growing pressure to reduce carbon emissions, and electric propulsion systems (EPS) based on proton exchange membrane fuel cells (PEMFCs) provide a promising path toward sustainable, zero-carbon aviation. However, challenges related to hydrogen storage and transport have hindered the practical implementation of such systems. Ammonia (NH<sub>3</sub>), with high energy density, convenient storage and transport, and carbon neutrality, has emerged as an attractive hydrogen carrier. This study proposes and experimentally validates a compact NH<sub>3</sub> cracking power generation system tailored for EPS through laboratory-scale exploration, engineering-scale validation, and system-level evaluation. The system delivers a maximum power output of 30 kW and comprises a custom-designed multifunctional NH<sub>3</sub> cracking reactor with integrated heat recovery, a temperature swing adsorption (TSA) purification unit, and PEMFC stacks. To meet practical application needs, this study screens and optimizes a commercially available 1% Ru–Ni/Al<sub>2</sub>O<sub>3</sub> catalyst, achieving over 99% NH<sub>3</sub> conversion under realistic conditions. The TSA unit reduces NH<sub>3</sub> concentration to below the detection limit, ensuring stable PEMFC performance with a single-stack maximum power output of 5.3 kW. Simulation results further show that the multi-stage thermal management increases the propulsion usable net electrical efficiency to 20.52%, and further raises the overall energy efficiency to 28.33% when the low-grade recovered heat is assumed fully usable. The optimized system achieves a gravimetric energy density of 692.7 W·h·kg<sup>−1</sup> and a hydrogen storage capacity of 6.7 wt% when equipped with five NH<sub>3</sub> tanks, each containing 22.7 kg of NH<sub>3</sub>. This work demonstrates an NH<sub>3</sub>-powered PEMFC EPS for aviation, offering both experimental validation and theoretical guidance for NH<sub>3</sub>-fueled propulsion technologies. The study provides system-level insights into design, integration, and performance optimization, supporting the future development of electrified aviation and related zero–carbon distributed energy systems.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"4 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-05DOI: 10.1016/j.eng.2026.02.019
Jiarui Li, Ze Xiang, Yunyang Xu, Xingyu Luo, Yao Jiang, Yingchen Huang, Zhe Yang, Ronggao Chen, Xiao Xu
Cellular senescence, a state of irreversible cell cycle arrest, is increasingly recognized as a key pathological driver of the progression of chronic liver diseases from metabolic dysfunction and fibrosis to hepatocellular carcinoma. While initially acting as a tumor-suppressive mechanism to eliminate damaged cells, the chronic accumulation of senescent cells creates a proinflammatory, profibrotic microenvironment through the senescence-associated secretory phenotype (SASP), thereby promoting tissue damage. This review examines the context-dependent mechanisms of cellular senescence across a range of chronic liver diseases, including metabolic, immune-mediated, viral, and malignant conditions. Building on these mechanisms, we critically assess the therapeutic landscape—from the selective clearance of senescent cells to novel strategies that modulate the senescence program—highlighting both their promise and current limitations. Despite the generally promising preclinical results, the clinical translation of senotherapies faces significant hurdles, including the heterogeneity of senescence, a lack of specific biomarkers, and potential off-target effects. Overcoming these challenges through emerging technologies will be crucial to harnessing senescence as a new therapeutic axis for chronic liver disease.
{"title":"Targeting Cellular Senescence: A New Therapeutic Axis in Chronic Liver Disease","authors":"Jiarui Li, Ze Xiang, Yunyang Xu, Xingyu Luo, Yao Jiang, Yingchen Huang, Zhe Yang, Ronggao Chen, Xiao Xu","doi":"10.1016/j.eng.2026.02.019","DOIUrl":"https://doi.org/10.1016/j.eng.2026.02.019","url":null,"abstract":"Cellular senescence, a state of irreversible cell cycle arrest, is increasingly recognized as a key pathological driver of the progression of chronic liver diseases from metabolic dysfunction and fibrosis to hepatocellular carcinoma. While initially acting as a tumor-suppressive mechanism to eliminate damaged cells, the chronic accumulation of senescent cells creates a proinflammatory, profibrotic microenvironment through the senescence-associated secretory phenotype (SASP), thereby promoting tissue damage. This review examines the context-dependent mechanisms of cellular senescence across a range of chronic liver diseases, including metabolic, immune-mediated, viral, and malignant conditions. Building on these mechanisms, we critically assess the therapeutic landscape—from the selective clearance of senescent cells to novel strategies that modulate the senescence program—highlighting both their promise and current limitations. Despite the generally promising preclinical results, the clinical translation of senotherapies faces significant hurdles, including the heterogeneity of senescence, a lack of specific biomarkers, and potential off-target effects. Overcoming these challenges through emerging technologies will be crucial to harnessing senescence as a new therapeutic axis for chronic liver disease.","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"69 1","pages":""},"PeriodicalIF":12.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147360278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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