Engineering最新文献

Rheumatoid arthritis (RA), a globally increasing autoimmune disorder, is associated with increased disability rates due to the disruption of iron metabolism. Tripterygium glycoside tablets (TGTs), a Tripterygium wilfordii Hook. f. (TwHF)-based therapy, exhibit satisfactory clinical efficacy for RA treatment. However, drug-induced liver injury (DILI) remains a critical issue that hinders the clinical application of TGTs, and the molecular mechanisms underlying the efficacy and toxicity of TGTs in RA have not been fully elucidated. To address this problem, we integrated clinical multi-omics data associated with the anti-RA efficacy and DILI of TGTs with the chemical and target profiling of TGTs to perform a systematic network analysis. Subsequently, we identified effective and toxic targets following experimental validation in a collagen-induced arthritis (CIA) mouse model. Significantly different transcriptome–protein–metabolite profiles distinguishing patients with favorable TGTs responses from those with poor outcomes were identified. Intriguingly, the clinical efficacy and DILI of TGTs against RA were associated with metabolic homeostasis between iron and bone and between iron and lipids, respectively. Particularly, the signal transducer and activator of transcription 3 (STAT3)–hepcidin (HAMP)/lipocalin 2 (LCN2)–tartrate-resistant acid phosphatase type 5 (ACP5) and STAT3–HAMP–acyl-CoA synthetase long-chain family member 4 (ACSL4)–lysophosphatidylcholine acyltransferase 3 (LPCAT3) axes were identified as key drivers of the efficacy and toxicity of TGTs. TGTs play dual roles in ameliorating CIA-induced pathology and in inducing hepatic dysfunction, disruption of lipid metabolism, and hepatic lipid peroxidation. Notably, TGTs effectively reversed “iron–bone” disruptions in the inflamed joint tissues of CIA mice by inhibiting the STAT3–HAMP/LCN2–ACP5 axis, subsequently leading to “iron–lipid” disturbances in the liver tissues via modulation of the STAT3–HAMP–ACSL4–LPCAT3 axis. Additional bidirectional validation experiments were conducted using MH7A and AML12 cells to confirm the bidirectional regulatory effects of TGTs on key targets. Collectively, our data highlight the association between iron-mediated metabolic homeostasis and the clinical efficacy and toxicity of TGT in RA therapy, offering guidance for the rational clinical use of TwHF-based therapy with dual therapeutic and toxic potential.

The issue of opacity within data-driven artificial intelligence (AI) algorithms has become an impediment to these algorithms’ extensive utilization, especially within sensitive domains concerning health, safety, and high profitability, such as chemical engineering (CE). In order to promote reliable AI utilization in CE, this review discusses the concept of transparency within AI utilizations, which is defined based on both explainable AI (XAI) concepts and key features from within the CE field. This review also highlights the requirements of reliable AI from the aspects of causality (i.e., the correlations between the predictions and inputs of an AI), explainability (i.e., the operational rationales of the workflows), and informativeness (i.e., the mechanistic insights of the investigating systems). Related techniques are evaluated together with state-of-the-art applications to highlight the significance of establishing reliable AI applications in CE. Furthermore, a comprehensive transparency analysis case study is provided as an example to enhance understanding. Overall, this work provides a thorough discussion of this subject matter in a way that—for the first time—is particularly geared toward chemical engineers in order to raise awareness of responsible AI utilization. With this vital missing link, AI is anticipated to serve as a novel and powerful tool that can tremendously aid chemical engineers in solving bottleneck challenges in CE.

This study demonstrates the feasibility and effectiveness of utilizing native soils as a resource for inocula to produce n-caproate through the chain elongation (CE) platform, offering new insights into anaerobic soil processes. The results reveal that all five of the tested soil types exhibit CE activity when supplied with high concentrations of ethanol and acetate, highlighting the suitability of soil as an ideal source for n-caproate production. Compared with anaerobic sludge and pit mud, the native soil CE system exhibited higher selectivity (60.53%), specificity (82.32%), carbon distribution (60.00%), electron transfer efficiency (165.00%), and conductivity (0.59 ms∙cm⁻¹). Kinetic analysis further confirmed the superiority of soil in terms of a shorter lag time and higher yield. A microbial community analysis indicated a positive correlation between the relative abundances of Pseudomonas, Azotobacter, and Clostridium and n-caproate production. Moreover, metagenomics analysis revealed a higher abundance of functional genes in key microbial species, providing direct insights into the pathways involved in n-caproate formation, including in situ CO₂ utilization, ethanol oxidation, fatty acid biosynthesis (FAB), and reverse beta-oxidation (RBO). The numerous functions in FAB and RBO are primarily associated with Pseudomonas, Clostridium, Rhodococcus, Stenotrophomonas, and Geobacter, suggesting that these genera may play roles that are involved or associated with the CE process. Overall, this innovative inoculation strategy offers an efficient microbial source for n-caproate production, underscoring the importance of considering CE activity in anaerobic soil microbial ecology and holding potential for significant economic and environmental benefits through soil consortia exploration.

Scaling up clean hydrogen supply in the near future is critical to achieving China’s hydrogen development target. This study established an electrolytic hydrogen development mechanism considering the generation mix and operation optimization of power systems with access to hydrogen. Based on the incremental cost principle, we quantified the provincial and national clean hydrogen production cost performance levels in 2030. The results indicated that this mechanism could effectively reduce the production cost of clean hydrogen in most provinces, with a national average value of less than 2 USD·kg⁻¹ at the 40-megaton hydrogen supply scale. Provincial cooperation via power transmission lines could further reduce the production cost to 1.72 USD·kg⁻¹. However, performance is affected by the potential distribution of hydrogen demand. From the supply side, competitiveness of the mechanism is limited to clean hydrogen production, while from the demand side, it could help electrolytic hydrogen fulfil a more significant role. This study could provide a solution for the ambitious development of renewables and the hydrogen economy in China.

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportunities. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics (MD) simulation trajectories. The analysis reveals that DES systems have two unique features compared to non-DES systems: The DESs have ① more imbalance between the numbers of the two intra-component HBs and ② more and stronger inter-component HBs. Based on these results, we develop 30 ML models using ten algorithms and three types of HB-based descriptors. The model performance is first benchmarked using the average and minimal receiver operating characteristic (ROC)-area under the curve (AUC) values. We also analyze the importance of individual features in the models, and the results are consistent with the simulation-based statistical analysis. Finally, we validate the models using the experimental data of 34 systems. The extra trees forest model outperforms the other models in the validation, with an ROC-AUC of 0.88. Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

The rapid development of the global economy and population growth are accompanied by the production of numerous waste textiles. This leads to a waste of limited resources and serious environmental pollution problems caused by improper disposal. The rational recycling of wasted textiles and their transformation into high-value-added emerging products, such as smart wearable devices, is fascinating. Here, we propose a novel roadmap for turning waste cotton fabrics into three-dimensional elastic fiber-based thermoelectric aerogels by a one-step lyophilization process with decoupled self-powered temperature–compression strain dual-parameter sensing properties. The thermoelectric aerogel exhibits a fast compression response time of 0.2 s, a relatively high Seebeck coefficient of 43 μV·K^−1, and an ultralow thermal conductivity of less than 0.04 W·m⁻¹·K⁻¹. The cross-linking of trimethoxy(methyl)silane (MTMS) and cellulose endowed the aerogel with excellent elasticity, allowing it to be used as a compressive strain sensor for guessing games and facial expression recognition. In addition, based on the thermoelectric effect, the aerogel can perform temperature detection and differentiation in self-powered mode with the output thermal voltage as the stimulus signal. Furthermore, the wearable system, prepared by connecting the aerogel-prepared array device with a wireless transmission module, allows for temperature alerts in a mobile phone application without signal interference due to the compressive strains generated during gripping. Hence, our strategy is significant for reducing global environmental pollution and provides a revelatory path for transforming waste textiles into high-value-added smart wearable devices.

Lunar habitat construction is crucial for successful lunar exploration missions. Due to the limitations of transportation conditions, extensive global research has been conducted on lunar in situ material processing techniques in recent years. The aim of this paper is to provide a comprehensive review, precise classification, and quantitative evaluation of these approaches, focusing specifically on four main approaches: reaction solidification (RS), sintering/melting (SM), bonding solidification (BS), and confinement formation (CF). Eight key indicators have been identified for the construction of low-cost and high-performance systems to assess the feasibility of these methods: in situ material ratio, curing temperature, curing time, implementation conditions, compressive strength, tensile strength, curing dimensions, and environmental adaptability. The scoring thresholds are determined by comparing the construction requirements with the actual capabilities. Among the evaluated methods, regolith bagging has emerged as a promising option due to its high in situ material ratio, low time requirement, lack of high-temperature requirements, and minimal shortcomings, with only the compressive strength falling below the neutral score. The compressive strength still maintains a value of 2–3 MPa. The proposed construction scheme utilizing regolith bags offers numerous advantages, including rapid and large-scale construction, ensured tensile strength, and reduced reliance on equipment and energy. In this study, guidelines for evaluating regolith solidification techniques are provided, and directions for improvement are offered. The proposed lunar habitat design based on regolith bags is a practical reference for future research.

Biodegradable polylactic acid (PLA) melt-blown nonwovens are attractive candidates to replace non-degradable polypropylene melt-blown nonwovens. However, it is still an extremely challenging task to prepare PLA melt-blown nonwovens with sufficient mechanical properties for practical application. Herein, we report a simple strategy for the large-scale preparation of biodegradable PLA/poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) melt-blown nonwovens with high strength and excellent toughness. In this process, a small amount of PHBV is added to PLA to improve the latter’s crystallization rate and crystallinity. In addition, when the PHBV content increases from 0 to 7.5 wt%, the diameters of the PLA/PHBV melt-blown fibers decrease significantly (with the proportion of nanofibers increasing from 7.7% to 42.9%). The resultant PLA/PHBV (5 wt% PHBV) melt-blown nonwovens exhibit the highest mechanical properties. The tensile stress, elongation, and toughness of PLA/PHBV (5 wt% PHBV) melt-blown nonwovens reach 2.5 MPa, 45%, and 1.0 MJ·m⁻³, respectively. More importantly, PLA/PHBV melt-blown nonwovens can be completely degraded into carbon dioxide and water after four months in the soil, making them environmentally friendly. A general tensile-failure model of melt-blown nonwovens is proposed in this study, which may shed light on mechanical performance enhancement for nonwovens.

The question of whether an ideal network exists with global scalability in its full life cycle has always been a first-principles problem in the research of network systems and architectures. Thus far, it has not been possible to scientifically practice the design criteria of an ideal network in a unimorphic network system, making it difficult to adapt to known services with clear application scenarios while supporting the ever-growing future services with unexpected characteristics. Here, we theoretically prove that no unimorphic network system can simultaneously meet the scalability requirement in a full cycle in three dimensions—the service-level agreement (S), multiplexity (M), and variousness (V)—which we name as the “impossible SMV triangle” dilemma. It is only by transforming the current network development paradigm that the contradiction between global scalability and a unified network infrastructure can be resolved from the perspectives of thinking, methodology, and practice norms. In this paper, we propose a theoretical framework called the polymorphic network environment (PNE), the first principle of which is to separate or decouple application network systems from the infrastructure environment and, under the given resource conditions, use core technologies such as the elementization of network baselines, the dynamic aggregation of resources, and collaborative software and hardware arrangements to generate the capability of the “network of networks.” This makes it possible to construct an ideal network system that is designed for change and capable of symbiosis and coexistence with the generative network morpha in the spatiotemporal dimensions. An environment test for principle verification shows that the generated representative application network modalities can not only coexist without mutual influence but also independently match well-defined multimedia services or custom services under the constraints of technical and economic indicators.