ACS Engineering Au最新文献

Direct reduction of chromite (DRC) is a promising alternative process for ferrochrome production with the potential to significantly reduce energy consumption and greenhouse gas emissions compared to conventional smelting. In DRC, chromium (Cr) and iron (Fe) from chromite ore incongruently dissolve into a molten salt, which facilitates mass transfer to a carbon (C) reductant where in situ metallization occurs. Consequently, ferrochrome is produced below the slag melting temperatures, achieving substantial energy savings relative to smelting. However, there are significant knowledge gaps in the kinetics, Cr solubility, speciation, and coordination environment which are critical to understanding the fundamental mechanisms of molten salt-assisted carbothermic reactions. To address these knowledge gaps, we performed pyrometallurgical experiments with variable temperature and residence times and analyzed the composition of chromite, ferrochrome, and slag products along with determining the speciation of Cr. Our results indicate that the DRC mechanism can be explained by the following sequential steps: (1) incongruent dissolution of chromite, (2) reduction of dissolved Cr in molten salt/slag, (3) transport of Cr and Fe species in molten media, and (4) reduction on C particles and metallization as Cr–Fe alloys. The discovery of four types of reduced Cr species in the slag indicates that the reduction of Cr³⁺ to Cr²⁺ and Cr⁰ occurred in the molten phase before metallization on solid carbon particles. Thermodynamically, the reduction of CrO(l) to Cr metal is more feasible at a lower temperature than it is for Cr₂O₃(l) corroborating the accelerated reduction efficiency of the DRC process.

This Review provides an in-depth overview of carbon dioxide (CO₂) capture, utilization, and sequestration (CCUS) technologies and their potential in global decarbonization efforts. The Review discusses the concept of CO₂ utilization, including conversion to fuels, chemicals, and minerals as well as biological processes. It also explores the different types of CO₂ sequestration, including geological, ocean, and mineral storage, and the associated challenges and opportunities such as regulatory issues and public acceptance. The Review highlights the potential of integrating CO₂ CCUS technologies and presents case studies of successful projects. The benefits and limitations of these technologies are discussed, along with areas for further research and development. Overall, this Review underscores the importance of CCUS.

The continuous growth of industrial activities, driven by economic expansion and technological advancements, has increased industrial waste generation. These wastes often contain hazardous substances, including heavy metals. Their improper disposal has become a significant environmental and health concern, necessitating global attention. To address this issue and mitigate the scarcity and cost of raw materials, recycling waste materials has emerged as a viable solution, particularly in the synthesis of construction materials. Various methods, such as pyrometallurgical and hydrometallurgical techniques, have been established for recycling industrial waste. This Review focuses on hydrometallurgical techniques, specifically targeting the separation of two highly toxic heavy metals: chromium and vanadium. It comprehensively explores various hydrometallurgical methods, including acid, alkaline, organic, and oxidative leaching, for solid waste materials. Additionally, this Review highlights several intensified leaching processes assisted by electrical fields, supercritical fluids, plasma, microwaves, and ultrasound. The presented methods offer promising approaches to effectively manage industrial waste.

In this study, a cost-effective and scalable method for the production of low-layer graphene (LLG) using sodium percarbonate (SPC) as a green delamination agent and its application in fuel cells is proposed. The obtained graphene showed a decrease in signal height in XRD analysis, indicating thinner layers. Raman analysis confirmed the presence of 7–8 layers of graphene. Field-emission scanning electron microscopy analysis revealed a uniform crystal structure, making it suitable for various applications. Direct methanol fuel cells (DMFCs) are widely recognized as efficient and environmentally friendly devices for converting chemical energy to electrical energy. The utilization of graphene-supported platinum (Pt) nanoparticles (NPs) as catalysts in DMFCs enhances their performance. In this study, Pt-graphene catalysts were synthesized by the chemical reduction method with graphene obtained by using SPC. Characterization through XRD and SEM analyses confirmed the homogeneous distribution of NPs on the carbon support. As a result of methanol oxidation studies, 57.73 and 21.45 mA/cm² values were obtained by using Pt@LLG and Pt catalysts, respectively. As a result of long-term stability and durability tests, it has been found that the Pt@LLG catalyst can be used effectively in metal oxidation experiments.

Machine learning (ML) surrogate models are used for the rapid prediction of materials properties and are promising tools for accelerating new materials design and development. The performance and accuracy of these surrogate models appear to be intricately connected to the molecular representation that is employed. Developing efficient numerical representations of molecules is vital for the success of surrogate models in predicting materials' properties. Here, we propose a new machine-readable molecular representation, namely a molecular quick response (QR) code, for the deep learning of materials structure–property correlations. We built a convolutional deep neural network (CNN) model based on molecular QR codes, which is abbreviated as QRChEM. QRChEM was trained and validated using ∼21 000 data for four representative properties of small molecules, namely specific heat, enthalpy, zero-point vibrational energy, and HOMO–LUMO band gap. We show that QRChEM outperforms the commonly used Morgan fingerprint-based and one-hot encoding (OHE)-based deep learning frameworks. We further performed UMAP (uniform manifold approximation and projection) on the molecular QR codes to demonstrate the differentiability of the molecular topologies, which is vital for high-fidelity surrogate model development.

The introduction of molecular additives into thermosets often results in changes in their dynamics and mechanical properties that can have significant ramifications for diverse applications of this broad class of materials such as coatings, high-performance composites, etc. Currently, there is limited fundamental understanding of how such additives influence glass formation in these materials, a problem of broader significance in glass-forming materials. To address this fundamental problem, here, we employ a simplified coarse-grained (CG) model of a polymer network as a model of thermoset materials and then introduce a polymer additive having the same inherent rigidity and polymer–polymer interaction strength as the cross-linked polymer matrix. This energetically “neutral” or “self-plasticizing” additive model gives rise to non-trivial changes in the dynamics of glass formation and provides an important theoretical reference point for the technologically more important case of interacting additives. Based on this rather idealized model, we systematically explore the combined effect of varying the additive mass percentage (m) and cross-link density (c) on the segmental relaxation dynamics and mechanical properties of a model thermoset material with additives. We find that increasing the additive mass percentage m progressively decreases both the glass-transition temperature T_g and the fragility of glass formation, a trend opposite to increasing c so that these thermoset variables clearly have a competing effect on glass formation in these model materials. Moreover, basic mechanical properties (i.e., bulk, shear, and tensile moduli) likewise exhibit a competitive variation with the increase of m and c, which are strongly correlated with the Debye–Waller parameter ⟨u²⟩, a measure of material stiffness at a molecular scale. Our findings prove beneficial in the development of structure–property relationships for the cross-linked polymers, which could help guide the design of such network materials with tailored physical properties.

The supply of the heat required for chemical processes via renewable electricity, i.e., process electrification, provides an alternative strategy for replacing conventional fossil fuel combustion. This approach enables fast, selective, and uniform heating, offers great potential for utilizing the excess renewable electric energy, and brings about an important chance for mitigating CO₂ emissions. In this work, we provide an overview of the state-of-the-art electricity-to-heat driven catalytic processes. The principle and fundamentals of Joule heating are provided and briefly compared to induction and microwave heating in view of electrifying catalytic processes. By this comparison, we assess that Joule heating can be regarded as the most promising method for process electrification, and its applications to methane reforming, cracking reactions, CO₂ valorization, and transient process operation are then reviewed. Advantages and disadvantages are critically addressed in terms of efficiency, potential for scale-up and possibility of retrofitting. The current challenges in the development of advanced electrified processes as well as the opportunities of next generation electrification techniques are discussed.

A number of methodologies are currently being exploited in order to dramatically increase the composition space explored in the design of new battery materials. This is proving necessary as commercial Li-ion battery materials have become increasingly high-performing and complex. For example, commercial cathode materials have quinary compositions with a sixth element in the coating, while a very large number of contenders are still being considered for solid electrolytes, with most of the periodic table being at play. Furthermore, the promise of accelerated design by computation and machine learning (ML) are encouraging, but they both ultimately require large amounts of quality experimental data either to fill in holes left by the computations or to be used to improve the ML models. All of this leads researchers to increase experimental throughputs. This perspective focuses on semiautomated experimental approaches where automation is only utilized in key steps where absolutely necessary in order to overcome bottlenecks while minimizing costs. Such workflows are more widely accessible to research groups as compared to fully automated systems, such that the current perspective may be useful to a wide community. The most essential steps in automation are related to characterization, with X-ray diffraction being a key bottleneck. By analyzing published workflows of both semi- and fully automated workflows, it is found herein that steps handled by researchers during the synthesis are not prohibitive in terms of overall throughput and may lead to greater flexibility, making more synthesis routes possible. Examples will be provided in this perspective of workflows that have been optimized for anodes, cathodes, and electrolytes in Li batteries, the vast majority of which are also suitable for battery technologies beyond Li.

The worldwide emphasis on reducing greenhouse gas (GHG) emissions has increased focus on the potential to mitigate emissions through climate-smart agricultural practices, including regenerative, digital, and controlled environment farming systems. The effectiveness of these solutions largely depends on their ability to address environmental concerns, generate economic returns, and meet supply chain needs. In this Review, we summarize the state of knowledge on the GHG impacts and profitability of these three existing and emerging farming systems. Although we find potential for CO₂ mitigation in all three approaches (depending on site-specific and climatic factors), we point to the greater level of research covering the efficacy of regenerative and digital agriculture in tackling non-CO₂ emissions (i.e., N₂O and CH₄), which account for the majority of agriculture’s GHG footprint. Despite this greater research coverage, we still find significant methodological and data limitations in accounting for the major GHG fluxes of these practices, especially the lifetime CH₄ footprint of more nascent climate-smart regenerative agriculture practices. Across the approaches explored, uncertainties remain about the overall efficacy and persistence of mitigation─particularly with respect to the offsetting of soil carbon sequestration gains by N₂O emissions and the lifecycle emissions of controlled environment agriculture systems compared to traditional systems. We find that the economic feasibility of these practices is also system-specific, although regenerative agriculture is generally the most accessible climate-smart approach. Robust incentives (including carbon credit considerations), investments, and policy changes would make these practices more financially accessible to farmers.

CO₂ capture is an emerging technology to reduce the effects of CO₂ emissions on the atmosphere. Amine resins could play an important role to realize this goal not as a storage material but as an option to produce highly concentrated CO₂ streams which can be used further in the chain. Air oxidation is a major point of concern with respect to the operational lifetime of the resins and its economic viability. The oxidation of the resins follows the so-called Basic Autoxidation Scheme or Free Radical Chain Autoxidation scheme which consists of three steps: (1) Initiation, (2) Propagation, and (3) Termination. From both bioinorganic chemistry and oxidation catalysis, it is known that Initiation of Free Radical Chain Autoxidation is the step with the highest activation energy. In the limiting case, Initiation occurs at high temperature via H-abstraction by O₂ itself. Experimentally obtained activation barriers on oxidative degradation for Branched Polyethylene Imine and Lewatit R VP OC 1065 are 135.0 and 122.7 kJ/mol, respectively. The computational values for Branched Polyethylene Imine and Lewatit R VP OC 1065 are 133.2 and 117.5 kJ/mol, respectively. Transition metal ions like Fe(II)/Fe(III) play an important role in Initiation, leading to much lower activation barriers. Two plausible types of Initiation with Fe(II)/Fe(III) were investigated by comparing previously published experimental findings with newly obtained computational results. The two mechanisms are (1) Outer Sphere Electron Transfer by Fe(III) and (2) Dioxygen Activation by Fe(II). It was found that the Outer Sphere Electron Transfer mechanism is very unlikely as no applicable exothermic reaction between Fe(III) complexes and an amine resin model could be determined. Dioxygen Activation by Fe(II) complexes of primary amines in Branched PolyEthylene Imine, most likely, is responsible for the Initiation of oxidative degradation of amine resins under Direct Air Capture CO₂ process conditions. The computational activation barrier for Dioxygen Activation of a Branched Polyethylene Imine model is 68.6 kJ/mol. The latter is much lower than the experimentally obtained activation barriers for Branched Polyethylene Imine and Lewatit R VP OC 1065 in their limiting cases. Molecular Modeling was able to make a clear distinction between the various initiation processes. This provides an improved understanding of oxidative degradation of Branched Polyethylene Imine and Lewatit R VP OC 1065 in general. It also provides an outlook to the application of Polyethylene Imine resins in Direct Air Capture CO₂ processes. The upfront removal of all possible initiators should lead to drastically increased lifetimes. From the activation barrier of Branched Polyethylene Imine as determined experimentally and computationally, a lifetime of approximately 5 years between 30 and 50 °C seems possible under ideal process conditions.