Hasil untuk "Chemical industries"

J. Abbott

Walter G. Blacconiere, Dennis M. Patten

J. Penner, D. Lister, E. Griggs et al.

R. Veugelers, B. Cassiman

Stijn Van de Vyver, J. Geboers, P. Jacobs et al.

Bhupinder Kaur, Fazilah Ariffin, R. Bhat et al.

Y. Xing, C. Kolstad

��ngela Anglada, A. Urtiaga, I. Ortiz

Z. Guzel‐Seydim, A. Greene, A. C. Seydim

Alice Di Bella, Toni Seibold, Tom Brown et al.

This study analyzes how Europe can decarbonize its industrial sector while remaining competitive. Using the open-source model PyPSA-Eur, it examines key energy- and emission-intensive industries, including steel, cement, methanol, ammonia, and high-value chemicals. Two development paths are explored: a continued decline in industrial activity and a reindustrialization driven by competitiveness policies. The analysis assesses cost gaps between European green products and lower-cost imports, and evaluates strategies such as intra-European relocation, selective imports of green intermediates, and targeted subsidies. Results show that deep industrial decarbonization is technically feasible, led by electrification, but competitiveness depends strongly on policy choices. Imports of green intermediates can lower costs while preserving jobs and production, whereas broad subsidies are economically unsustainable. Effective policy should focus support on sectors like ammonia and steel finishing while maintaining current production levels.

Patricia Mayer, Florian Joseph Baader, David Yang Shu et al.

The chemical industry's transition to net-zero greenhouse gas (GHG) emissions is particularly challenging due to the carbon inherently contained in chemical products, eventually released to the environment. Fossil feedstock-based production can be replaced by electrified chemical production, combining carbon capture and utilization (CCU) with electrolysis-based hydrogen. However, electrified chemical production requires vast amounts of clean electricity, leading to competition in our sector-coupled energy systems. In this work, we investigate the pathway of the chemical industry towards electrified production within the context of a sector-coupled national energy system's transition to net-zero emissions. Our results show that the sectors for electricity, low-temperature heat, and mobility transition before the chemical industry due to the required build-up of renewables, and to the higher emissions abatement of heat pumps and battery electric vehicles. To achieve the net-zero target, the energy system relies on clean energy imports to cover 41\% of its electricity needs, largely driven by the high energy requirements of a fully electrified chemical industry. Nonetheless, a partially electrified industry combined with dispatchable production alternatives provides flexibility to the energy system by enabling electrified production when renewable electricity is available. Hence, a partially electrified, diversified chemical industry can support the integration of intermittent renewables, serving as a valuable component in net-zero energy systems.

Xing-yang Cui, Jin-chao Ma, Shu-yue Xu et al.

There is a growing interest in the development of metal-doped carbon nitrides due to their extensive applications in photocatalysis, organocatalysis, electrocatalysis, nanozymes, and biosensing technologies. Here a series of transition metal-doped carbon nitride nanocomposite has been developed utilizing a rapid combustion synthesis method. Comprehensive characterization of the samples was conducted utilizing techniques such as X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy-energy dispersion X-ray spectrometry, transmission electron microscopy, X-ray photoelectron spectroscopy, and Brunauer–Emmett–Teller specific surface area measurement analysis. The catalytic effects of the composites were evaluated through TG and DSC analyses. Among the composite materials studied, PCN-0.50Cu with the unique bivalent state structure of copper (Cu(0) and CuO(II)) demonstrated the highest catalytic activity, resulting in a reduction of the peak decomposition temperature of ammonium perchlorate (AP) by 101.5 °C, a decrease in the duration of thermal decomposition by 54.7 %, and a reduction in activation energy by 141.5 kJ mol−1. Moreover, the incorporation of PCN-1.00Cu at a concentration of 2 wt% resulted in a 45.47 % enhancement in the combustion rate of HTPB-based solid propellant. The combustion synthesis of transition metal-doped carbon nitride nanocomposites constitutes an effective approach for the development of solid propellants catalysts and advances the potential applications of nanocomposites in high-energy solid propellant technologies.

Hu Qihan

With the increasing severity of climate change and the threat of global warming, countries are boosting the development and promotion of renewable energy sources like solar and wind to bring about the energy transition and reduce carbon emissions. However, because these energy sources are unreliable in supply, efficient energy storage technologies are required to balance energy output to satisfy daily electricity demand. Among numerous energy storage alternatives, rechargeable battery technology has received a lot of interest due to its high energy density, efficiency, and reusable nature. Currently, lithium-ion batteries, nickel-metal hydride batteries, lead-acid batteries, and other rechargeable battery types are widely utilized in consumer electronics, transportation, renewable energy storage, medical equipment, and other industries. This paper examines the basic working principle, structure, and important chemical reactions of rechargeable batteries, as well as their performance indicators such as capacity, energy density, cycle life, and so on. Furthermore, this paper addresses the technology’s global application cases, as well as the status of its development and the major problems, such as safety, cost, longevity, environmental impact, and other issues. Finally, this article examines probable future technology breakthroughs, market trends, and the possibility of suitable policy support in this field.

D. Braga, L. Maini, F. Grepioni

Sindhana Pannir-Sivajothi, Joel Yuen-Zhou

An important question in polariton chemistry is whether reacting molecules are in thermal equilibrium with their surroundings. If not, can experimental changes observed in reaction rates of molecules in a cavity (even without optical pumping) be attributed to a higher/lower temperature inside the cavity? In this work, we address this question by computing temperature differences between reacting molecules inside a cavity and the air outside. We find this temperature difference to be negligible for most reactions. On the other hand, for phase transitions inside cavities, as the temperature of the material is actively maintained by a heating/cooling source in experiments, we show cavities can modify observed transition temperatures when mirrors and cavity windows are ideal (non-absorbing); however, this modification vanishes when real mirrors and windows are used. Finally, we find substantial differences in blackbody spectral energy density between free space and infrared cavities, which reveal resonance effects and could potentially play a role in explaining changes in chemical reactivity in the dark.

Sanggyu Chong, Filippo Bigi, Federico Grasselli et al.

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (e.g. atomic, body-ordered, or range-separated) predictions are made. We leverage these metrics to understand the learning behavior of different ML models, and to guide efficient dataset construction for model training. We finally implement the formalism for a ML model targeting a coarse-grained system to demonstrate the applicability of the prediction rigidities to an even broader class of atomistic modeling problems.

Kai Yuan, Shuai Zhou, Ning Li et al.

Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine-learning (ML) assisted scheduling optimization [Ref: J. Comput. Chem. 2023, 44, 1174], we further propose 1) the improve ML models for the better predictions of computational loads, and as such, more elaborate load-balancing calculations can be expected; 2) the idea of coded computation, i.e. the integration of gradient coding, in order to introduce fault tolerance during the distributed calculations; and 3) their applications together with re-normalized exciton model with time-dependent density functional theory (REM-TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. The results show that the improved ML-assisted coded calculations can further improve the load-balancing and cluster utilization, and owing primarily profit in fault tolerance that aiming at the automated quantum chemical calculations for both ground and excited states.

Xin Chen, Jessica Martinez, Xuecheng Shao et al.

We present a reformulation of QM/MM as a fully quantum mechanical theory of interacting subsystems, all treated at the level of density functional theory (DFT). For the MM subsystem, which lacks orbitals, we assign an ad hoc electron density and apply orbital-free DFT functionals to describe its quantum properties. The interaction between the QM and MM subsystems is also treated using orbital-free density functionals, accounting for Coulomb interactions, exchange, correlation, and Pauli repulsion. Consistency across QM and MM subsystems is ensured by employing data-driven, many-body MM force fields that faithfully represent DFT functionals. Applications to water-solvated systems demonstrate that this approach achieves unprecedented, very rapid convergence to chemical accuracy as the size of the QM subsystem increases. We validate the method with several pilot studies, including water bulk, water clusters (prism hexamer and pentamers), solvated glucose, a palladium aqua ion, and a wet monolayer of MoS$_2$.

Danyang Wang, Dongqi Yu, Menghan Xu et al.

Ethanol sensors have found extensive applications across various industries, including the chemical, environmental, transportation, and healthcare sectors. With increasing demands for enhanced performance and reduced energy consumption, there is a growing need for developing new ethanol sensors. Micro-electromechanical system (MEMS) devices offer promising prospects in gas sensor applications due to their compact size, low power requirements, and seamless integration capabilities. In this study, SnO₂-TiO₂ nanocomposites with varying molar ratios of SnO₂ and TiO₂ were synthesized via ball milling and then printed on MEMS chips for ethanol sensing using electrohydrodynamic (EHD) printing. The study indicates that the two metal oxides dispersed evenly, resulting in a well-formed gas-sensitive film. The SnO₂-TiO₂ composite exhibits the best performance at a molar ratio of 1:1, with a response value of 25.6 to 50 ppm ethanol at 288 °C. This value is 7.2 times and 1.8 times higher than that of single SnO₂ and TiO₂ gas sensors, respectively. The enhanced gas sensitivity can be attributed to the increased surface reactive oxygen species and optimized material resistance resulting from the chemical and electronic effects of the composite.

MingShuai Xie, HongChao Luo, XinJuan Liu et al.

Abstract Under the dual pressures of environmental protection and energy security, the development and application of coal-based nanocarbon materials, supported by the technical concepts of molecular chemical engineering and nanomaterial science, is of significant importance for achieving the high-value clean utilization of coal. Furthermore, it serves as an effective means to assist in the realization of dual carbon goals. Coal, with its abundant reserves, high carbon content, and aromatic and hydrogenated aromatic groups, exhibits great advantages and potential in the synthesis of nanocarbon materials. In addition to its applications in traditional power and chemical industries, coal-based nanocarbon materials also demonstrate significant value in the field of environmental pollution control. This article succinctly summarizes the preparation methods and properties of coal-based carbon nanotubes, coal-based carbon quantum dots, and coal-based graphene, elucidates their current applications in water pollution control and governance, and anticipates their development trends in water pollution control, aiming to provide support for the clean and efficient utilization of coal and water pollution control.