ACS Engineering Au最新文献

Polymer networks are widely used in applications, and the formation of a network and its gel point can be predicted. However, the effects of spatial and topological heterogeneity on the resulting network structure and ultimately the mechanical properties, are less understood. To address this challenge, we generate in silico random networks of cross-linked polymer chains with controlled spatial and topological defects. While all fully reacted networks investigated in this study have the same number of end-functionalized polymer strands and cross-linkers, we vary the degree of spatial and topological heterogeneities systematically. We find that spatially heterogeneous cross-linker distributions result in a reduction in the network’s primary loops with increased spatial heterogeneity, the opposite trend as observed in homogeneous networks. By performing molecular dynamics simulations, we investigated the mechanical properties of the networks. Even though spatially heterogeneous networks have more elastically active strands and cross-linkers, they break at lower extensions than the homogeneous networks and sustain slightly lower maximum stresses. Their shear moduli are higher, i.e., stiffer, than theoretically predicted, and higher than their homogeneous gel counterparts. Our results highlight that topological loop defects and spatial heterogeneities result in significantly different network structures and, ultimately, different mechanical properties.

The efficient capture of HCHO, tobacco smoke, and anthropogenic toxic pollutants is of paramount importance to mitigate indoor air pollution and protect the general population. Ultralight N-doped graphene aerogel (N-GA) with a three-dimensional (3D) honeycomb-like coarse-pore structure is synthesized from biomass (pear). By taking advantage of the micrometer-sized honeycomb pores, 3D interconnected porous structure, hierarchical pores, large pore volume (0.81 cm³ g^–1), highly accessible surface area (1582 m² g^–1), and heteroatom-enriched (1.89% of N and 9.88% of O) nature, the N-GA offered high adsorption of the toxic gaseous compounds (TGCs). The as-synthesized N-GA without any further chemical/physical treatment exhibits an excellent adsorption-based capture of TGCs such as HCHO (996.7 mg g^–1), ethanol (611 mg g^–1), tobacco smoke (523.8 mg g^–1), benzene (482.3 mg g^–1), toluene (392 mg g^–1), and carbon dioxide (365.3 mg g^–1). Moreover, N-GA, as a low-cost and renewable adsorbent, exhibits high recyclability and long-term adsorption efficiency. These results demonstrate the potential of N-GA as an unprecedented candidate to design high-performance adsorbents for TGCs, suggesting a great application potential in air filters to control both indoor and outdoor air pollution.

Atmospheric pressure plasmas have shifted in recent years from being a burgeoning research field in the academic setting to an actively investigated technology in the chemical, oil, and environmental industries. This is largely driven by the climate change mitigation efforts, as well as the evident pathways of value creation by converting greenhouse gases (such as CO₂) into useful chemical feedstock. Currently, most high technology readiness level (TRL) plasma-based technologies are based on volumetric and power-based scaling of thermal plasma systems, which results in large capital investment and regular maintenance costs. This work investigates bringing a quasi-thermal (so-called “warm”) plasma setup, namely, a gliding arc plasmatron, from a lab-scale to a pilot-scale capacity with an increase in throughput capacity by a factor of 10. The method of scaling is the parallelization of plasmatron reactors within a single housing, with the aim of maintaining a warm plasma regime while simultaneously improving build cost and efficiency (compared to separate reactors operating in parallel). Special attention is also given to the safety and control features implemented in the setup, a key component required for integration into industrial systems. The performance of the multi-reactor gliding arc plasmatron (MRGAP) reactor is investigated, focusing on the influence of flow rate and the number of active reactors. The location of active reactors was deemed to have a negligible effect on the monitored metrics of conversion, energy efficiency, and energy cost. The optimum operating conditions were found to be with the most active reactors (five) at the highest investigated flow rate (80 L/min). Analysis of results suggests that an optimum conversion (9%) and plug power-based energy efficiency (19%) can be maintained at a specific energy input (SEI) around 5.3 kJ/L (or 1 eV/molecule). The concept of parallelization of plasmatron reactors within a singular housing was demonstrated to be a viable method for scaling up from a lab-scale to a prototype-scale device, with performance analysis suggesting that increasing the power (through adding more reactor channels) and total flow rate, while maintaining an SEI around 5.3 or 4.2 kJ/L, i.e., 1.3 or 1 eV/molecule (based on plug power and plasma-deposited power, respectively), can result in increased conversion rate without sacrificing absolute conversion or energy efficiency.

Most secondary batteries in academia are (dis)charged by applying a constant current (CC) followed by a constant voltage (CV), i.e., a CCCV procedure. The usual concept is then to condense data for interpretation into representations such as differential capacity, or dQ/dV, graphs. This is done to extract information related to phenomena such as the growth of the solid electrolyte interphase or, more broadly, degradation. Typically, these measurements take several months because measurements for differential capacity analysis need to be performed at relatively low C-rates. An alternate charging schedule to CCCV is pulsed charging, where CC sections are interrupted by an open-circuit measurement on a second time scale. These and similar partially constant current strategies primarily target diffusive effects during charging and broadly fall into a linear charging category, where the time derivative for the actuated property is mostly zero. Herein, the author explores nonlinear charging, i.e., the process of actively applying a potential with a nontrivial time derivate and a resulting nontrivial current time derivative, to engineer (dis)charge cycles with enhanced information density. This method of nonlinear charging is then used to charge a cell such that some potential ranges in the differential capacity diagram are omitted. This study is purely a simulative endeavor and not backed by experimentation owing mainly to the lack of facile implementation of arbitrary function inputs for battery cyclers and might point to limitations of the underlying theory. If found to be confirmed through an experiment, then this technique would, however, motivate a new roadmap to better understand secondary battery degradation inspired by electrocatalyst degradation.

The global Net Zero transformation is a vital response to the climate change crisis. Australia and India face similar challenges due to their reliance on fossil resources, growing energy demand, and agricultural emissions. However, differences exist in the population, industry, and development. This perspective explores these comparisons between Australia and India’s Net Zero aspirations and current sociopolitical and economic drivers. As part of the portfolio of options needed to address net zero goals, Carbon Capture, Utilization, and Storage (CCUS) and Carbon Dioxide Removal (CDR) solutions are specifically discussed. The perspective concludes with opportunities for the nations to engage in knowledge sharing and bilateral partnerships to help accelerate the world’s transformation to Net Zero.

We propose a numerical strategy based on dynamic load balancing (DLB) aimed at enhancing the computational efficiency of multiscale CFD simulation of reactive flows at catalyst surfaces. Our approach employs DLB combined with a hybrid parallelization technique, integrating both MPI and OpenMP protocols. This results in an optimized distribution of the computational load associated with the chemistry solution across processors, thereby minimizing computational overheads. Through assessments conducted on fixed and fluidized bed reactor simulations, we demonstrated a remarkable improvement of the parallel efficiency from 19 to 87% and from 19 to 91% for the fixed and fluidized bed, respectively. Owing to this improved parallel efficiency, we observe a significant computational speed-up of 1.9 and 2.1 in the fixed and fluidized bed reactor simulations, respectively, compared to simulations without DLB. All in all, the proposed approach is able to improve the computational efficiency of multiscale CFD simulations paving the way for a more efficient exploitation of high-performance computing resources and expanding the current boundaries of feasible simulations.

Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design, and manufacturing, including their capacity to work effectively with human language, symbols, code, and numerical data. Here, we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. Moreover, when used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem-solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how fine-tuning endows LLMs with a reasonable understanding of subject area knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty recalling correct information and may hallucinate. We show how this can be addressed using retrieval-augmented Ontological Knowledge Graph strategies. The graph-based strategy helps us not only to discern how the model understands what concepts are important but also how they are related, which significantly improves generative performance and also naturally allows for injection of new and augmented data sources into generative AI algorithms. We find that the additional feature of relatedness provides advantages over regular retrieval augmentation approaches and not only improves LLM performance but also provides mechanistic insights for exploration of a material design process. Illustrated for a use case of relating distinct areas of knowledge, here, music and proteins, such strategies can also provide an interpretable graph structure with rich information at the node, edge, and subgraph level that provides specific insights into mechanisms and relationships. We discuss other approaches to improve generative qualities, including nonlinear sampling strategies and agent-based modeling that offer enhancements over single-shot generations, whereby LLMs are used to both generate content and assess content against an objective target. Examples provided include complex question answering, code generation, and execution in the context of automated force-field development from actively learned density functional theory (DFT) modeling and data analysis.

A rise in the disinfection of spaces occurred as a result of the COVID-19 pandemic as well as an increase in people wearing facial coverings. Hydrogen peroxide was among the recommended disinfectants for use against the virus. Previous studies have investigated the emissions of hydrogen peroxide associated with the disinfection of spaces and masks; however, those studies did not focus on the emitted byproducts from these processes. Here, we simulate the disinfection of an indoor space with H₂O₂ while a person wearing a face mask is present in the space by using an environmental chamber with a thermal manikin wearing a face mask over its breathing zone. We injected hydrogen peroxide to disinfect the space and utilized a chemical ionization mass spectrometer (CIMS) to measure the primary disinfectant (H₂O₂) and a Vocus proton transfer reaction time-of-flight mass spectrometer (Vocus PTR-ToF-MS) to measure the byproducts from disinfection, comparing concentrations inside the chamber and behind the mask. Concentrations of the primary disinfectant and the byproducts inside the chamber and behind the mask remained elevated above background levels for 2–4 h after disinfection, indicating the possibility of extended exposure, especially when continuing to wear the mask. Overall, our results point toward the time-dependent impact of masks on concentrations of disinfectants and their byproducts and a need for regular mask change following exposure to high concentrations of chemical compounds.