Hasil untuk "Chemical industries"

Guangda Niu, Xudong Guo, Liduo Wang

H. Kambo, Animesh Dutta

N. Plechkova, K. R. Seddon

Julia Radwan-Pragłowska, Paulina Bąk, Łukasz Janus et al.

Excessive blood loss is a leading cause of mortality among soldiers and accident victims. The wound healing process typically ranges from three weeks to several months, with disruptions in healing stages potentially prolonging recovery time. Chronic wounds may persist for years, creating a favorable environment for microbial growth. Chitosan, a derivative of chitin—the second most abundant biopolymer in nature—is obtained through deacetylation and exhibits mucoadhesive, analgesic, antioxidant, biodegradable, non-toxic, and biocompatible properties. Due to its hemostatic and regenerative support capabilities, chitosan is widely applied in the food, cosmetic, and agricultural industries; environmental protection; and as a key component in dressings for chronic wound healing. Notably, its antibacterial properties make it a promising candidate for novel biomaterials to replace traditional antibiotics and prevent the emergence of drug-resistant strains. The primary aim of this study was the chemical cross-linking of chitosan with the amino acids L-aspartic and L-glutamic acid in the presence of periclase (magnesium oxide) under microwave radiation conditions. Subsequent research stages involved the analysis of the samples’ physicochemical properties using SEM, FT-IR, XPS, atomic absorption spectrometry, swelling behavior (in water, SBF, and blood), porosity, and density. Biological assessments included biodegradation, cytotoxicity, and antibacterial activity against Escherichia coli and Staphylococcus aureus. The obtained results confirmed the high potential of the newly developed hemostatic agents for effective hemorrhage management under non-sterile conditions.

Shuye Zhang, Xinyue Li, Fugang Lu et al.

Femtosecond laser welding, a novel technique for material joining, faces challenges such as stringent pre-welding requirements and low joint strength when directly welding ceramics. In this study, we addressed the issues associated with the direct welding of yttria-stabilized zirconia (YSZ) and sapphire by first depositing a nanometer-thick Ti layer on the ceramic surface, followed by femtosecond laser welding. Notably, we observed for the first time that femtosecond laser pulses induced the formation of a honeycomb structure at the interface, forming a YSZ/sapphire micro-welding joint characterized by a continuous structure, a honeycomb structure, and Ti-rich phases. This specific joint distribution significantly enhanced the interface transition and improved the joint strength. Under conditions of 8 W laser power, scanning speed of 50 mm/s, and pulse frequency of 200 kHz, the micro-welding joint exhibited optimal interface performance, achieving a maximum shear strength of approximately 79 MPa. Through calculations of the temperature distribution of the interface and the surface energy of the crystal, we conclude that the honeycomb structure arises from the Ti layer, the temperature gradient distribution, and the tendency of sapphire to melt along directions with lower surface energy. The honeycomb structure effectively enriched the transition between the micro-welding interface and the substrate. The new findings of this study offer valuable insights and potential pathways for the reliable and efficient welding of advanced ceramics.

N. Diepstraten, L.M.T. Somers, J.A. van Oijen

The hydrogen-fueled direct-injection (DI) compression-ignition (CI) argon power cycle (APC) is a highly efficient and emission-free energy conversion system that relies on the low specific heat capacity of the working fluid argon. Compared to conventional internal combustion engines, the DI CI APC allows to easily adjust the oxygen level of the charge. Besides, the engine operating pressure can be increased at virtually no cost assuming the mechanics of the engine are suitable. In this study, we systematically investigate how to take advantage of these two benefits by varying the intake pressure and oxygen mole fraction using a validated Reynolds-averaged numerical simulation environment. It is found that the optimal amount of oxygen is a trade-off between burn duration and specific heat ratio of the charge. Provided that the oxygen mole fraction is high enough to achieve complete combustion, the sensitivity of thermal efficiency to oxygen mole fraction is relatively low. Increasing the intake pressure for a fixed oxygen level leads to shorter burn durations and less heat loss, thereby significantly increasing thermal efficiency. Flame–wall interaction is a dominant factor that negatively impacts the engine performance, and should therefore be minimized. The highest obtained thermal efficiency is 70%, which is comparable to but mostly higher than efficiencies of state-of-the-art hydrogen fuel cells. The DI CI APC has therefore potential to overcome challenges of conventional engines, without penalizing its strengths.

Alexius Wadell, Anoushka Bhutani, Victor Azumah et al.

Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches lack the scalability required to efficiently navigate chemical space. Scientific foundation models trained on large unlabeled datasets offer a path toward exploring chemical space across diverse application domains. Here we develop MIST, a family of molecular foundation models with up to an order of magnitude more parameters and data than prior works. Trained using a novel tokenization scheme that comprehensively captures nuclear, electronic, and geometric information, MIST learns from a diverse range of molecules. MIST models have been fine-tuned to predict more than 400 structure -- property relationships and match or exceed state-of-the-art performance across benchmarks spanning physiology, electrochemistry, and quantum chemistry. We demonstrate the ability of these models to solve real-world problems across chemical space, including multiobjective electrolyte solvent screening, olfactory perception mapping, isotope half-life prediction, stereochemical reasoning for chiral organometallic compounds, and binary and multi-component mixture property prediction. Probing MIST models using mechanistic interpretability methods reveals identifiable patterns and trends not explicitly present in the training data, suggesting that the models learn generalizable scientific concepts. We formulate hyperparameter-penalized Bayesian neural scaling laws and use them to reduce the computational cost of model development by an order of magnitude. The methods and findings presented here represent a significant step toward accelerating materials discovery, design, and optimization using foundation models and provide valuable guidance for training compute-optimal scientific foundation models.

Nawaf Alampara, Anagha Aneesh, Martiño Ríos-García et al.

Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, fuzzy datasets that are difficult to leverage in conventional machine learning approaches. A new class of models, which can be summarized under the term general-purpose models (GPMs) such as large language models, has shown the ability to solve tasks they have not been directly trained on, and to flexibly operate with low amounts of data in different formats. In this review, we discuss fundamental building principles of GPMs and review recent and emerging applications of those models in the chemical sciences across the entire scientific process. While many of these applications are still in the prototype phase, we expect that the increasing interest in GPMs will make many of them mature in the coming years.

Hyewon Jung, Seungil Ahn, Seungho Choi et al.

Fire incident reports contain detailed textual narratives that capture causal factors often overlooked in structured records, while financial damage amounts provide measurable outcomes of these events. Integrating these two sources of information is essential for uncovering interpretable links between descriptive causes and their economic consequences. To this end, we develop a data-driven framework that constructs a composite Risk Index, enabling systematic quantification of how specific keywords relate to property damage amounts. This index facilitates both the identification of high-impact terms and the aggregation of risks across semantically related clusters, thereby offering a principled measure of fire-related financial risk. Using more than a decade of Korean fire investigation reports on the chemical industry classified as Special Buildings (2013 through 2024), we employ topic modeling and network-based embedding to estimate semantic similarities from interactions among words and subsequently apply Lasso regression to quantify their associations with property damage amounts, thereby estimate fire risk index. This approach enables us to assess fire risk not only at the level of individual terms but also within their broader textual context, where highly interactive related words provide insights into collective patterns of hazard representation and their potential impact on expected losses. The analysis highlights several domains of risk, including hazardous chemical leakage, unsafe storage practices, equipment and facility malfunctions, and environmentally induced ignition. The results demonstrate that text-derived indices provide interpretable and practically relevant insights, bridging unstructured narratives with structured loss information and offering a basis for evidence-based fire risk assessment and management.

Shuan Chen, Kye Sung Park, Taewan Kim et al.

Accurately predicting chemical reaction outcomes and potential byproducts is a fundamental task of modern chemistry, enabling the efficient design of synthetic pathways and driving progress in chemical science. Reaction mechanism, which tracks electron movements during chemical reactions, is critical for understanding reaction kinetics and identifying unexpected products. Here, we present Reactron, the first electron-based machine learning model for general reaction prediction. Reactron integrates electron movement into its predictions, generating detailed arrow-pushing diagrams that elucidate each mechanistic step leading to product formation. We demonstrate the high predictive performance of Reactron over existing product-only models by a large-scale reaction outcome prediction benchmark, and the adaptability of the model to learn new reactivity upon providing a few examples. Furthermore, it explores combinatorial reaction spaces, uncovering novel reactivities beyond its training data. With robust performance in both in- and out-of-distribution predictions, Reactron embodies human-like reasoning in chemistry and opens new frontiers in reaction discovery and synthesis design.

Seenivasan Hariharan, Sebastian Zur, Sachin Kinge et al.

We lay the foundation for a quantum algorithmic framework to analyse fixed-structure chemical reaction networks (CRNs) using quantum random walks (QRWs) via electrical circuit theory. We model perturbations to CRNs, such as, species injections that shift steady-state concentrations, while keeping the underlying species-reaction graph fixed. Under physically meaningful mass-action constraints, we develop quantum algorithms that (i) decide reachability of target species after perturbation, (ii) sample representative reachable species, (iii) approximate steady-state fluxes through reactions, and (iv) estimate total Gibbs free-energy consumption. Our approach offers new tools for analysing the structure and energetics of complex CRNs, and opens up the prospect of scalable quantum algorithms for chemical and biochemical reaction networks.

Manuel Palma Banos, Joel D. Kress, Rigoberto Hernandez et al.

A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic symbolic regression in which a sparse analytical form for a dynamical system is discoverable from the underlying data. We specifically develop the stoichiometrically-informed symbolic regression (SISR) method to address a standing challenge in complex chemical reaction networks: Given a time-series dataset of concentrations of several components, what is the mechanism and the associated rate constants? SISR finds the optimal mechanism, kinetic equations and rate constants by combining differential optimization with a genetic optimization approach that searches a symbolic space of possible reaction mechanisms. Use of SISR in several paradigmatic examples spanning linear and nonlinear reaction schemes results in excellent agreement between true and predicted mechanisms, including when the method is applied to noisy data. The advantages of a stoichiometrically-informed approach such as SISR to address reaction discovery is illustrated through comparison with the use of generic state-of-the-art data-driven approaches.

Ryoichi Tanaka, Yuta Mizuno, Takuro Tsutsumi et al.

Transition state theory is a standard framework for predicting the rate of a chemical reaction. Although the transition state theory has been successfully applied to numerous chemical reaction analyses, many experimental and theoretical studies have reported chemical reactions with a reactivity which cannot be explained by the transition state theory due to dynamic effects. Dynamical systems theory provides a theoretical framework for elucidating dynamical mechanisms of such chemical reactions. In particular, reactive islands are essential phase space structures revealing dynamical reaction patterns. However, the numerical computation of reactive islands in a reaction system of many degrees of freedom involves an intrinsic challenge -- the curse of dimensionality. In this paper, we propose a dimensionality reduction algorithm for computing reactive islands in a reaction system of many degrees of freedom. Using the supervised principal component analysis, the proposed algorithm projects reactive islands into a low-dimensional phase space with preserving the dynamical information on reactivity as much as possible. The effectiveness of the proposed algorithm is examined by numerical experiments for Hénon-Heiles systems extended to many degrees of freedom. The numerical results indicate that our proposed algorithm is effective in terms of the quality of reactivity prediction and the clearness of the boundaries of projected reactive islands. The proposed algorithm is a promising elemental technology for practical applications of dynamical systems analysis to real chemical systems.

Daniel Marti-Dafcik, Nicholas Lee, Hugh G. A. Burton et al.

Molecular orbital theory is powerful both as a conceptual tool for understanding chemical bonding, and as a theoretical framework for ab initio quantum chemistry. Despite its undoubted success, MO theory has well documented shortcomings, most notably that it fails to correctly describe diradical states and homolytic bond fission. In this contribution, we introduce a generalised MO theory that includes spin-coupled radical states. We show through archetypical examples that when bonds break, the electronic state transitions between a small number of valence configurations, characterised by occupation of both delocalised molecular orbitals and spin-coupled localised orbitals. Our theory provides a model for chemical bonding that is both chemically intuitive and qualitatively accurate when combined with ab initio theory. Although exploitation of our theory presents significant challenges for classical computing, the predictable structure of spin-coupled states is ideally suited to algorithms that exploit quantum computers. Our approach provides a systematic route to overcoming the initial state overlap problem and unlocking the potential of quantum computational chemistry.

Nigel Rambhujun, M. Salman, Ting Wang et al.

Hydrogen is often touted as the fuel of the future, but hydrogen is already an important feedstock for the chemical industry. This review highlights current means for hydrogen production and use, and the importance of progressing R&D along key technologies and policies to drive a cost reduction in renewable hydrogen production and enable the transition of chemical manufacturing toward green hydrogen as a feedstock and fuel. The chemical industry is at the core of what is considered a modern economy. It provides commodities and important materials, e.g., fertilizers, synthetic textiles, and drug precursors, supporting economies and more broadly our needs. The chemical sector is to become the major driver for oil production by 2030 as it entirely relies on sufficient oil supply. In this respect, renewable hydrogen has an important role to play beyond its use in the transport sector. Hydrogen not only has three times the energy density of natural gas and using hydrogen as a fuel could help decarbonize the entire chemical manufacturing, but also the use of green hydrogen as an essential reactant at the basis of many chemical products could facilitate the convergence toward virtuous circles. Enabling the production of green hydrogen at cost could not only enable new opportunities but also strengthen economies through a localized production and use of hydrogen. Herein, existing technologies for the production of renewable hydrogen including biomass and water electrolysis, and methods for the effective storage of hydrogen are reviewed with an emphasis on the need for mitigation strategies to enable such a transition.

Sarah Thorwall, Cory M Schwartz, J. Chartron et al.

Fehim Findik

High-temperature materials play a significant role in sustainable engineering across various industries and applications. Sustainable engineering aims to design, develop, and implement solutions that minimize environmental impact, enhance resource efficiency, and promote long-term sustainability. The availability of substances that can be used efficiently at high temperatures allows pushing the limits of possible measurable demands. These substances include ceramics, polymers and metals. It is used in elevated temperature materials, aircraft and space structures, and space exploration. In this study, high temperature metals are classified including superalloys, platinum and refractory metals, refractory metals such as W, Nb, Mo, Ta. Also, ceramic materials are high temperature materials. Ceramics are criticized to use in elevated temperature due to their high hardness, extraordinary strength in compression, excellent thermal stability, short-term thermal extension and tremendously great melting temperature. Ceramics that encounter these standards are carbides and borides of Zr, Nb, Ta, Ti and Hf. In addition, steel, nickel and copper alloys used in aircraft engines, space shuttles and turbine blades from aerospace materials were investigated. In addition, powder metallurgy and sintering techniques, which are the most widely used production methods of high temperature materials, are emphasized. In this study, important characterization techniques for analyzing some sample surface and subsurface properties are reviewed. Again, in this study, the use of AES, XPS, SSIMS and LEED methods for the chemical examination of surfaces is discussed. Optical, electron, and scanning probe microscopy is used for pictorial inspection of inspection specimens and structures, obtaining data on surface, shape, colors, and numerous additional physical properties. Here, AFM, SEM, TEM, EDX, FIB and EMP methods are discussed. Among the material analysis devices, XRD, x-ray fluorescence spectrometry, low energy electron diffraction, neutron diffraction and electron microprobe devices were examined.

Vitaly Y. Prikhodko, Josh A. Pihl, Todd J. Toops et al.

A three-way catalyst (TWC) and a TWC with a NOx storage component (NS-TWC) were evaluated on a lean spark ignition (SI) engine platform to reduce the fuel consumption and emissions of a passive selective catalytic reduction (pSCR) emission control system. The pSCR system is an approach for controlling NOx emissions from lean SI engines. It relies on onboard NH3 generation over a TWC during brief periods of fuel-rich operation. The NH3 is then stored on a downstream SCR catalyst and is available for NOx reduction during subsequent periods of lean engine operation. The NS-TWC addition enabled longer lean operation and more efficient NH3 use, which lowered fuel penalty of the pSCR system. Over a pseudo-transient drive cycle, the lean SI engine with pSCR that included NS-TWC demonstrated a 8.3% reduction in gasoline consumption over stoichiometric-only engine operation, and the NOx and non-CH4 organic gas emissions were consistent with Environmental Protection Agency (EPA) Tier 3 levels. The CO emissions, primarily from rich operation, exceeded the EPA Tier 3 levels. A cleanup catalyst (CUC) with high oxygen storage capacity was used to oxidize tailpipe CO during rich excursions by using the stored oxygen from the preceding lean operation. Although the CUC decreased CO emissions and reduced NH3 slip, some of the NH3 was converted back to NOx. Furthermore, rich CO control remains challenging. The results of this work demonstrate significant improvement in fuel consumption and emissions with a modified pSCR system architecture.

Auxiliadora Gómez Morón

Mahya Karimzadeh khosroshahi, Mohammad Ebrahim Aghababaei

In the past few years, several major domestics and international challenges have emerged, causing global political and economic uncertainty. Economic uncertainty, defined as the difficulty in predicting the economic environment, arises from various factors such as political instability, changes and uncertainties in government policies, natural disasters, and market fluctuations. The presence of such uncertainties significantly affects the efficiency of markets, including the efficiency of the capital market. The aim of this study is to examine the long-term relationship between of economic policies uncertainty (based on the fluctuations of macroeconomic variables using the composite PCA index) and the excess return of eight different industries index (Automobile and parts manufacturing, Pharmaceutical Products and Materials , cement, lime and gypsum, Multidisciplinary industrial companies , basic metals, oil, coke, and nuclear fuel, coke and nuclear fuels, chemical products, Aggregation, properties and real estate).The investigation, conducted using the econometric ARDL approach over the period from 2012 to 2021, demonstrates that economic policies uncertainty is positively and significantly related to the excess returns of the selected industry index Among the various industries, the Automobile and manufacturing parts industry is most affected by the of economic policies uncertainty, while the construction and real estate industry is least affected. Furthermore, the speed of adjustment of Aggregation, properties and real estate the effect of Economic policy uncertainty on the excess returns of the stock market industries is not homogeneous, as indicated by the ECM coefficient. Automobile and manufacturing parts industry index experiences the fastest adjustment, while the Multidisciplinary industrial companies, due to their diverse portfolios, exhibit the slowest adjustment speed compared to others..