Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes, and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility. Computational high-throughput screening presents a viable solution to this challenge, as machine learning (ML) has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information. This review focuses on recent progress in applying ML in adsorption energy prediction, which predominantly quantifies the catalytic potential of a solid catalyst. ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed. At the end of the review, an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied. We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and, ultimately, reshape the chemical industry and energy landscape.
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.
Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design. However, the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties. Moreover, accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods. This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach. A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds. Prior selection techniques, including prior elicitation and prior predictive checking, are also applied during the building procedure to provide the model with more information from previous research findings. The framework is assessed using datasets of varying sizes for 20 pure component properties. For 18 out of the 20 pure component properties, the new models are found to give improved accuracy and predictive power in comparison with other published models, with and without machine learning.
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.
In this paper, we propose mesoscience-guided deep learning (MGDL), a deep learning modeling approach guided by mesoscience, to study complex systems. When establishing sample dataset based on the same system evolution data, different from the operation of conventional deep learning method, MGDL introduces the treatment of the dominant mechanisms of complex system and interactions between them according to the principle of compromise in competition (CIC) in mesoscience. Mesoscience constraints are then integrated into the loss function to guide the deep learning training. Two methods are proposed for the addition of mesoscience constraints. The physical interpretability of the model-training process is improved by MGDL because guidance and constraints based on physical principles are provided. MGDL was evaluated using a bubbling bed modeling case and compared with traditional techniques. With a much smaller training dataset, the results indicate that mesoscience-constraint-based model training has distinct advantages in terms of convergence stability and prediction accuracy, and it can be widely applied to various neural network configurations. The MGDL approach proposed in this paper is a novel method for utilizing the physical background information during deep learning model training. Further exploration of MGDL will be continued in the future.
Reactive transport equations in porous media are critical in various scientific and engineering disciplines, but solving these equations can be computationally expensive when exploring different scenarios, such as varying porous structures and initial or boundary conditions. The deep operator network (DeepONet) has emerged as a popular deep learning framework for solving parametric partial differential equations. However, applying the DeepONet to porous media presents significant challenges due to its limited capability to extract representative features from intricate structures. To address this issue, we propose the Porous-DeepONet, a simple yet highly effective extension of the DeepONet framework that leverages convolutional neural networks (CNNs) to learn the solution operators of parametric reactive transport equations in porous media. By incorporating CNNs, we can effectively capture the intricate features of porous media, enabling accurate and efficient learning of the solution operators. We demonstrate the effectiveness of the Porous-DeepONet in accurately and rapidly learning the solution operators of parametric reactive transport equations with various boundary conditions, multiple phases, and multi-physical fields through five examples. This approach offers significant computational savings, potentially reducing the computation time by 50-1000 times compared with the finite-element method. Our work may provide a robust alternative for solving parametric reactive transport equations in porous media, paving the way for exploring complex phenomena in porous media.
To equip data-driven dynamic chemical process models with strong interpretability, we develop a light attention-convolution-gate recurrent unit (LACG) architecture with three sub-modules-a basic module, a brand-new light attention module, and a residue module-that are specially designed to learn the general dynamic behavior, transient disturbances, and other input factors of chemical processes, respectively. Combined with a hyperparameter optimization framework, Optuna, the effectiveness of the proposed LACG is tested by distributed control system data-driven modeling experiments on the discharge flowrate of an actual deethanization process. The LACG model provides significant advantages in prediction accuracy and model generalization compared with other models, including the feedforward neural network, convolution neural network, long short-term memory (LSTM), and attention-LSTM. Moreover, compared with the simulation results of a deethanization model built using Aspen Plus Dynamics V12.1, the LACG parameters are demonstrated to be interpretable, and more details on the variable interactions can be observed from the model parameters in comparison with the traditional interpretable model attention-LSTM. This contribution enriches interpretable machine learning knowledge and provides a reliable method with high accuracy for actual chemical process modeling, paving a route to intelligent manufacturing.
Nickel-rich layered Li transition metal oxides are the most promising cathode materials for high-energy-density Li-ion batteries. However, they exhibit rapid capacity degradation induced by transition metal dissolution and structural reconstruction, which are associated with hydrofluoric acid (HF) generation from lithium hexafluorophosphate decomposition. The potential for thermal runaway during the working process poses another challenge. Separators are promising components to alleviate the aforementioned obstacles. Herein, an ultrathin double-layered separator with a polyimide (PI) basement and a polyvinylidene difluoride (PVDF) coating layer is designed and fabricated by combining a nonsolvent induced phase inversion process and coating method. The PI skeleton provides good stability against potential thermal shrinkage, and the strong PI-PVDF bonding endows the composite separator with robust structural integrity; these characteristics jointly contribute to the extraordinary mechanical tolerance of the separator at elevated temperatures. Additionally, unique HF-scavenging effects are achieved with the formation of hydrogen bonds for the abundant coordination sites provided by the imide ring; hence, the layered Ni-rich cathodes are protected from HF attack, which ultimately reduces transition metal dissolution and facilitates long-term cyclability of the Ni-rich cathodes. Li||NCM811 batteries (where "NCM" indicates ) with the proposed composite separator exhibit a capacity retention after 400 cycles at room temperature and remain sustainable at with a capacity retention after 200 cycles. By adopting a new perspective on separators, this study presents a feasible and promising strategy for suppressing capacity degradation and enabling the safe operation of Ni-rich cathode materials.
Oxy-combustion is a promising carbon-capture technology, but atmospheric-pressure oxy-combustion has a relatively low net efficiency, limiting its application in power plants. In pressurized oxy-combustion (POC), the boiler, air separation unit, flue gas recirculation unit, and purification and compression unit are all operated at elevated pressure; this makes the process more efficient, with many advantages over atmospheric pressure, such as low emissions, a smaller boiler size, and more. POC is also more promising for industrial application and has attracted widespread research interest in recent years. It can produce high-pressure with a purity of approximately , which can be used directly for enhanced oil recovery or geo-sequestration. However, the pollutant emissions must meet the standards for carbon capture, storage, and utilization. Because of the high oxygen and moisture concentrations in POC, the formation of acids via the oxidation and solution of and can be increased, causing the corrosion of pipelines and equipment. Furthermore, particulate matter (PM) and mercury emissions can harm the environment and human health. The main distinction between pressurized and atmospheric-pressure oxy-combustion is the former’s elevated pressure; thus, the effect of this pressure on the pollutants emitted from -including , and mercury-must be understood, and effective control methodologies must be incorporated to control the formation of these pollutants. This paper reviews recent advances in research on , and mercury formation and control in POC systems that can aid in pollutant control in such systems.
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 at the megaton hydrogen supply scale. Provincial cooperation via power transmission lines could further reduce the production cost to . 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.
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-resis tant 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.
Infections caused by intracellular bacterial pathogens are difficult to treat since most antibiotics have low cell permeability and undergo rapid degradation within cells. The rapid development and dissemination of antimicrobial-resistant strains have exacerbated this dilemma. With the increasing knowledge of host-pathogen interactions, especially bacterial strategies for survival and proliferation within host cells, host-directed therapy (HDT) has attracted increased interest and has emerged as a promising anti-infection method for treating intracellular infection. Herein, we applied a cell-based screening approach to a US Food and Drug Administration (FDA)-approved drug library to identify compounds that can inhibit the intracellular replication of Salmonella Typhimurium (S. Typhimurium). This screening allowed us to identify the antidiarrheal agent loperamide (LPD) as a potent inhibitor of . Typhimurium intracellular proliferation. LPD treatment of infected cells markedly promoted the host autophagic response and lysosomal activity. A mechanistic study revealed that the increase in host autophagy and elimination of intracellular bacteria were dependent on the high expression of glycoprotein nonmetastatic melanoma protein B (GPNMB) induced by LPD. In addition, LPD treatment effectively protected against . Typhimurium infection in Galleria mellonella and mouse models. Thus, our study suggested that LPD may be useful for the treatment of diseases caused by intracellular bacterial pathogens. Moreover, LPD may serve as a promising lead compound for the development of anti-infection drugs based on the HDT strategy.
Substantially glazed facades are extensively used in contemporary high-rise buildings to achieve attractive architectural aesthetics. Inherent conflicts exist among architectural aesthetics, building energy consumption, and solar energy harvesting for glazed facades. In this study, we addressed these conflicts by introducing a new dynamic and vertical photovoltaic integrated building envelope (dvPVBE) that offers extraordinary flexibility with weather-responsive slat angles and blind positions, superior architectural aesthetics, and notable energy-saving potential. Three hierarchical control strategies were proposed for different scenarios of the dvPVBE: power generation priority (PGP), natural daylight priority (NDP), and energy-saving priority (ESP). Moreover, the PGP and ESP strategies were further analyzed in the simulation of a dvPVBE. An office room integrated with a dvPVBE was modeled using EnergyPlus. The influence of the dvPVBE in improving the building energy efficiency and corresponding optimal slat angles was investigated under the PGP and ESP control strategies. The results indicate that the application of dvPVBEs in Beijing can provide up to of the annual energy demand of office rooms and significantly increase the annual net energy output by at least compared with static photovoltaic (PV) blinds. The concept of this novel dvPVBE offers a viable approach by which the thermal load, daylight penetration, and energy generation can be effectively regulated.
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 . 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.
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 mor-pha 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.
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 , a relatively high Seebeck coefficient of , and an ultralow thermal conductivity of less than . 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.
Biodegradable polylactic acid (PLA) melt-blown nonwovens are attractive candidates to replace nondegradable 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 , 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, , and , 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.
Light emitting diodes (LEDs) have accounted for most of the lighting market as the technology matures and costs continue to reduce. As a new type of e-waste, LED is a double-edged sword, as it contains not only precious and rare metals but also organic packaging materials. In previous studies, LED recycling focused on recovering precious and strategic metals while ignoring harmful substances such as organic packaging materials. Unlike crushing and other traditional methods, hydrothermal treatment can provide an environment-friendly process for decomposing packaging materials. This work developed a closed reaction vessel, where the degradation rate of plastic polyphthalamide (PPA) was close to , with nano- encapsulated in plastic PPA being efficiently recovered, while metals contained in LED were also recycled efficiently. Besides, the role of water in plastic PPA degradation that has been overlooked in current studies was explored and speculated in detail in this work. Environmental impact assessment revealed that the proposed recycling route for waste LED could significantly reduce the overall environmental impact compared to the currently published processes. Especially the developed method could reduce more than half the impact of global warming. Furthermore, this research provides a theoretical basis and a promising method for recycling other plastic-packaged e-waste devices, such as integrated circuits.
This study demonstrates the feasibility and effectiveness of utilizing native soils as a resource for inocula to produce -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 -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 , and conductivity . 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 -caproate production. Moreover, metagenomics analysis revealed a higher abundance of functional genes in key microbial species, providing direct insights into the pathways involved in -caproate formation, including in situ 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 -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.
As a precise and versatile tool for genome manipulation, the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) platform holds promise for modifying fish traits of interest. With the aim of reducing transgene introgression and controlling reproduction, upscaled disease resistance and reproductive intervention in catfish species have been studied to lower the potential environmental risks of the introgression of escapees as transgenic animals. Taking advantage of the CRISPR/Cas9-mediated system, we succeeded in integrating the cathelicidin gene (As-Cath) from an alligator (Alligator sinensis) into the target luteinizing hormone (lh) locus of channel catfish (Ictalurus punctatus) using two delivery systems assisted by double-stranded DNA (dsDNA) and single-stranded oligodeoxynu-cleotides (ssODNs), respectively. In this study, high knock in (KI) efficiency (22.38%, 64/286) but low on-target events was achieved using the ssODN strategy, whereas adopting a dsDNA as the donor template led to an efficient on-target KI (10.80%, 23/213). The on-target KI of As-Cath was instrumental in establishing the knockout catfish line,which displayed heightened disease resistance and reduced fecundity compared with the wild-type (WT) sibling fish. Furthermore, administration of human chorionic gonadotropin (HCG) and luteinizing hormone-releasing hormone analogue (LHRHa) can restore the reproduction of the transgenic fish line. Overall, we replaced the gene with an alligator cathelicidin transgene and then administered hormone therapy to move towards complete reproductive control of disease-resistant transgenic catfish in an environmentally responsible manner. This strategy not only effectively improves consumer-valued traits but also guards against unwanted introgression, providing a breakthrough in aquaculture genetics to confine fish reproduction and prevent the establishment of transgenic or domestic genotypes in the natural environment.
