Hasil untuk "Chemical industries"

Y. Shah, B. G. Kelkar, S. Godbole et al.

Hua Zhao, Shuqian Xia, Pei-sheng Ma

E. Merino

K. Fisher, C. Phillips

R. Kuziak, R. Kawalla, S. Waengler

Wang Wei, Jinlong Gong

M. Aresta, A. Dibenedetto, A. Angelini

David Kogan, Moshe Gottlieb

In this work, we have studied the viscoelastic behavior of chemically and physically crosslinked Poly(vinyl alcohol) (PVA) hydrogels near the critical gel point (GP) as well as further away from it, by means of small amplitude (SAOS) and large amplitude (LAOS) oscillatory shear experiments. Chemical crosslinking involved covalent bonding by means of glutaraldehyde as a crosslinker, while physical crosslinking was induced by freeze-thaw cycles. SAOS data analysis allowed evaluation of critical parameters such as the critical relaxation exponent n, gel strength S, and equilibrium modulus Ge, based on the dynamic self-similarity and fractal network structures at the GP. LAOS rheological data analysis showed that the chemically crosslinked system exhibited moderate strain-dependance due to the permanent covalent bonds, whereas the physically crosslinked system displayed significant strain-dependent nonlinearity due to strain dependent interactions at the crosslink entities. LAOS experiments, supported by Chebyshev coefficients and Lissajous-Bowditch plots, highlighted pronounced differences in nonlinear responses, underscoring the influence of crosslinking mechanisms on the network rheological behavior. The findings establish LAOS as a powerful tool for differentiating polymeric network structures, providing insights beyond those attained by conventional linear rheology. 50 pages 1 figures

Andrew Ma, Marin Soljačić

In the past decade, there has been a significant interest in the use of machine learning approaches in materials science research. Conventional deep learning approaches that rely on complex, nonlinear models have become increasingly important in computational materials science due to their high predictive accuracy. In contrast to these approaches, we have shown in a recent work that a remarkably simple learned heuristic rule -- based on the concept of topogivity -- can classify whether a material is topological using only its chemical composition. In this paper, we go beyond the topology classification scenario by also studying the use of machine learning to develop simple heuristic rules for classifying whether a material is a metal based on chemical composition. Moreover, we present a framework for incorporating chemistry-informed inductive bias based on the structure of the periodic table. For both the topology classification and the metallicity classification tasks, we empirically characterize the performance of simple heuristic rules fit with and without chemistry-informed inductive bias across a wide range of training set sizes. We find evidence that incorporating chemistry-informed inductive bias can reduce the amount of training data required to reach a given level of test accuracy.

Xinyu Zhang, Haiyang Jia, Liangrong Peng et al.

Previous studies have primarily focused on the nonequilibrium thermodynamics of chemical reaction networks (CRNs) occurring in closed systems. In contrast, CRNs in open systems exhibit much richer nonequilibrium phenomena due to sustained matter and energy exchange. Here, we bridge the quantitative relationships between essential thermodynamic quantities -- including the steady state, enthalpy, intrinsic Gibbs free energy, and entropy production rate -- in original mass-action equations and their PEA- or QSSA-reduced counterparts for open CRNs. Our analysis demonstrates that the thermodynamic structure, especially the second law of thermodynamics, of the full CRNs may not be preserved in reduced models when algebraic relations are imposed. Specifically, PEA-reduced models lose monotonicity in the intrinsic Gibbs free energy, whereas QSSA retains this property. These theoretical findings are further validated through analytical and numerical studies of two archetypal open systems: the Michaelis-Menten reactions and the phosphorylation-dephosphorylation cycle (PdPC). Our results provide a systematic framework for evaluating the fidelity of reduced models.

Rémi Schlama, Joshua W. Sin, Ryan P. Burwood et al.

Chemical reaction optimisation is essential for synthetic chemistry and pharmaceutical development, demanding the extensive exploration of many reaction parameters to achieve efficient and sustainable processes. We report $α$-PSO, a novel nature-inspired metaheuristic algorithm that augments canonical particle swarm optimisation (PSO) with machine learning (ML) for parallel reaction optimisation. Unlike black-box ML approaches that obscure decision-making processes, $α$-PSO uses mechanistically clear optimisation strategies through simple, physically intuitive swarm dynamics directly connected to experimental observables, enabling practitioners to understand the components driving each optimisation decision. We establish a theoretical framework for reaction landscape analysis using local Lipschitz constants to quantify reaction space "roughness", distinguishing between smoothly varying landscapes with predictable surfaces and rough landscapes with many reactivity cliffs. This analysis guides adaptive $α$-PSO parameter selection, optimising performance for different reaction topologies. Systematic evaluation of $α$-PSO across pharmaceutically relevant reaction benchmarks demonstrates competitive performance with state-of-the-art Bayesian optimisation methods, while two prospective high-throughput experimentation (HTE) campaigns showed that $α$-PSO identified optimal reaction conditions more rapidly than Bayesian optimisation. $α$-PSO combines the predictive capability of advanced black-box ML methods with interpretable metaheuristic procedures, offering chemists an effective framework for parallel reaction optimisation that maintains methodological clarity while achieving highly performant experimental outcomes. Alongside our open-source $α$-PSO implementation, we release $989$ new high-quality Pd-catalysed Buchwald-Hartwig and Suzuki reactions.

Malikussaid, Hilal Hudan Nuha, Isman Kurniawan

Large Language Models frequently generate outputs that appear scientifically reasonable yet violate fundamental principles--a phenomenon we characterize as the "plausibility-validity gap." This challenge proves especially acute in chemistry, where superficial correctness masks deeper errors in molecular structure, reaction mechanisms, and synthetic pathways. We present a systematic approach combining a reasoning-centric model architecture (Magistral Small) with Low-Rank Adaptation fine-tuning on a dual-domain dataset covering molecular properties and chemical transformations. Evaluation reveals substantial improvements: the fine-tuned system achieves 96.3% format adherence, 97.4% chemical validity, and 74.4% synthesis feasibility. Comparative analysis shows our approach outperforms specialized translation models like MolT5 (97.4% vs 77.2% validity) while achieving performance comparable to complex tool-augmented systems like ChemCrow (9.0/10 vs 9.24/10 expert rating) through a more transparent, efficient methodology. Results demonstrate a learning hierarchy where syntactic correctness develops before chemical understanding, which precedes synthetic planning capability. This work establishes a reproducible framework for transforming generalist language models into dependable scientific tools while identifying critical areas including stereochemical precision, knowledge currency, and computational accessibility as key challenges for future advancement.

Nicharee Thinnakornsutibutr, Arianna Su, Ankit Sharma et al.

Over the last few decades, efforts to improve fire safety have emphasized understanding material flammability through small-scale laboratory testing due to safety and cost constraints. However, extending these findings to real-world fire scenarios is challenging, as buoyancy-driven flows—critical to fire behaviors—vary significantly with scales. The objective of this numerical study is to understand the effects of buoyant flow on fire dynamics, specifically in the context of upward flame spread over vertically oriented solid fuels. To approach the problem incrementally, simulations are first performed for inert thermal plume (no combustion), followed by simulations of laminar upward flame spread over a thin solid material. The results show that the boundary layer thicknesses and flame standoff distance increase when gravity and pressure decrease, and scale with p−0.5g−0.25. The buoyant flow magnitude increases with gravity but remains the same when pressure is varied (UB∼p0g0.5). The results of upward flame spread simulations further show that reaction rate, flame temperature, and flame spread rate have positive correlations with both pressure and gravity. Interestingly, while the incident radiative heat flux on the solid surface (q˙fr″) increases with ambient pressure, it is insensitive to gravity. Nevertheless, q˙fr″ has a minor effect on the net heat flux, owing to the dominant contribution of convective heat input. Comparing cases of a constant p2g, distributions of convective and net heat fluxes, solid surface temperatures, and solid mass loss rates are generally similar, resulting in similar overall flame behaviors. However, thermal inertia and flame radiation loss are both lower in a reduced pressure environment. This leads to higher flame temperature and higher convective heat input in a small region near the flame base, weakening the effects of pressure on flame spread. Because of this, when applying p2g pressure modeling, the flame spread rate is higher in reduced pressure than in reduced gravity. The results further show that p1.8g modeling has a better performance in correlating fire dynamics.

Jiashuai Wang, Bo Wu, Lingfeng Yang et al.

Thermites are widely used in propellants, explosives and ignition materials because of their high heat release rate and good combustion efficiency. Structural control over thermites to achieve improved performance leads to a promising research area. Among these, core-shell structured composites have attracted wide attention due to their excellent properties and close contact among components. Herein, core-shell structured α-AlH3/Fe2O3 thermites were prepared, which exhibit high heat-release and excellent combustion performance. At an equivalence ratio of 2.0, the core-shell structured α-AlH3/Fe2O3 has the most heat release (1213.8 J/g) and the lowest reaction activation energy (147.5 kJ/mol). The ignited combustion performance of α-AlH3/Fe2O3 was notably strengthened by the shorter ignition delay period (11 ms). Interestingly, the core-shell structured α-AlH3/Fe2O3 was less sensitive to electrostatic discharge, which suggests that the core-shell structured α-AlH3/Fe2O3 reaches the goal of high energy release and electrostatic safety. The core-shell thermite system with α-AlH3 as metal fuel could provide an efficient alternative to hunt for thermites with high reactivity.

Abdul Jabbar, Shahid Iqbal, Umer Younas et al.

Adhatoda vasica (L.) Nees holds prominence as a medicinal plant in Unani and Ayurvedic traditions. This study evaluates conventional versus modern extraction processes for isolating bioactive constituents from Adhatoda vasica leaves (AVL), using soxhlet extraction (SE), cold maceration (CM), microwave assisted extraction (MAE), and ultrasound assisted extraction (UAE) techniques and evaluate their scavenging potential. The maximum extraction yield (34.56 %) was achieved using MAE, followed by UAE, SE, and CM. The radical scavenging potential of AVL bioactive compounds was evaluated using 2,2-Diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), iron chelating (ICS), and 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assays. Results indicated that DPPH and ABTS showed 38.45 ± 1.87 % and 76.19 ± 0.98 % inhibition, respectively. The reducing power capacity was observed as 461.56 ± 1.90 mmol AAE/10 g, while iron chelating activity was 65.3 ± 1.76 μg Na₂EDTA/10 g. The MAE fraction exhibited the highest contents of flavonoids (46.21 ± 1.96 μg ECE/g), phenolics (55.69 ± 1.54 mg GAE/g), tannins (5.77 ± 1.57 mg TAE/g), and ascorbic acid (36.4 ± 1.54 mg AA/100 g). Due to highest scavenging potential chemical profiling of MAE fraction was performed using gas chromatography–mass spectrometry (GC–MS) and high-performance liquid chromatography (HPLC). GC–MS analysis without derivatization identified fourteen compounds, while derivatization with N,O-bis(trimethylsilyl) trifluoro acetamide revealed twelve additional compounds; n-butanol and ethanol fractions exhibited two and four compounds, respectively. HPLC analysis confirmed the presence of major phytochemicals including quercetin, gallic acid, syringic acid, and vitamin C. The results exhibit that AVL are abundant with phenolic compounds, and microwave-assisted extraction are highly effective in maximizing the yield of bioactive components. Due to their potent radical scavenging properties, these bioactives hold significant potential for use in the food, pharmaceutical, and cosmetic industries.

Miguel Arcadio Rosado Mendoza, Irving Josue González-Chan, Jazmin Sallet Novelo-Castilla et al.

Water contamination by lead has been studied extensively due to the risks it can cause for humans and the environment. Although there are traditional methods for removing this contaminant, it is necessary to evaluate sustainable and ecological alternatives such as chitosan obtained from shrimp waste. Chitosan and vanillin-functionalized chitosan were synthesized from shrimp (Farfantepenaeus duorarum) residues and evaluated for their ability to remove lead ions from aqueous solutions. The polymers were characterized using Fourier Transform InfraRed (FT-IR) spectroscopy, Scanning Electron Microscopy (SEM), and X-Ray Diffraction (XRD). FT-IR analysis confirmed the presence of characteristic functional groups, including the amino group at 1630 cm-1 and the C=O stretch at 1654 cm-1. SEM images revealed a smooth surface for pure chitosan, while functionalized chitosan exhibited a rougher surface with cracks, indicating increased adsorption sites. XRD analysis showed a reduction in crystallinity for functionalized chitosan, with peaks at 2θ=20° decreasing in intensity. Adsorption mechanisms were studied using the Langmuir and the Freundlich isotherms. The maximum adsorption capacity is accomplished at pH = 5 with an equilibrium concentration of 2 g/L for both polymers. The Langmuir isotherm best fit the experimental data for pure chitosan, with a maximum adsorption capacity of 1.9121 mg/g, while the Freundlich isotherm was more suitable for functionalized chitosan, with a maximum adsorption capacity of 2.6049 mg/g. The results suggest that chitosan and vanillin-chitosan polymers derived from shrimp residues are effective and environmentally friendly adsorbents for lead ion removal from aqueous solutions.

Sunghwa Kang, Jinsu Kim

Artificial neural networks (NNs) can be implemented using chemical reaction networks (CRNs), where the concentrations of species act as inputs and outputs. In such biochemical computing, noise-robust computing is crucial due to the intrinsic and extrinsic noise present in chemical reactions. Previously suggested CRNs for feed-forward networks often utilized the rectified linear unit (ReLU) or discrete activation functions. However, one concern in this case is the discontinuities of the derivatives of those non-smooth functions, which can cause significant noise disruption during backpropagation. In this study, we propose a CRN that performs both feed-forward and training processes using smooth activation functions to avoid discontinuities in the backpropagation. All reactions occur in a single pot, and the reactions for training are bimolecular. Our case studies on XOR, Iris, MNIST datasets, and a non-linear regression model demonstrate that computation via the CRN (i) maintains accuracy despite noise in the reaction rates and the concentration of species and (ii) is insensitive to the choice of the running time and the magnitude of the noise in comparison to NNs with a non-smooth activation function. This work presents a noise-robust CRN for full NN computation, including backpropagation, paving the way for more stable and efficient biochemical computing systems.

Diata Traore, Olivier Adjoua, César Feniou et al.

Using GPU-accelerated state-vector emulation, we propose to embed a quantum computing ansatz into density-functional theory via density-based basis-set corrections (DBBSC) to obtain quantitative quantum-chemistry results on molecules that would otherwise require brute-force quantum calculations using hundreds of logical qubits. Indeed, accessing a quantitative description of chemical systems while minimizing quantum resources is an essential challenge given the limited qubit capabilities of current quantum processors. We provide a shortcut towards chemically accurate quantum computations by approaching the complete-basis-set limit through coupling the DBBSC approach, applied to any given variational ansatz, to an on-the-fly crafting of basis sets specifically adapted to a given system and user-defined qubit budget. The resulting approach self-consistently accelerates the basis-set convergence, improving electronic densities, ground-state energies, and first-order properties (e.g. dipole moments), but can also serve as a classical, a posteriori, energy correction to quantum hardware calculations with expected applications in drug design and materials science.

Haorui Wang, Marta Skreta, Cher-Tian Ser et al.

Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in molecular discovery, traverse chemical space by performing random mutations and crossovers, leading to a large number of expensive objective evaluations. In this work, we ameliorate this shortcoming by incorporating chemistry-aware Large Language Models (LLMs) into EAs. Namely, we redesign crossover and mutation operations in EAs using LLMs trained on large corpora of chemical information. We perform extensive empirical studies on both commercial and open-source models on multiple tasks involving property optimization, molecular rediscovery, and structure-based drug design, demonstrating that the joint usage of LLMs with EAs yields superior performance over all baseline models across single- and multi-objective settings. We demonstrate that our algorithm improves both the quality of the final solution and convergence speed, thereby reducing the number of required objective evaluations. Our code is available at http://github.com/zoom-wang112358/MOLLEO

Hye-Lim Yu, Woo-Seok Kang, Ju-Hyeon Lee et al.

Piezoelectricity offers an electromechanical coupling that is widely utilized in transducer applications. There has been a consistent demand for transparent piezoelectric materials for optoelectrical applications. Therefore, despite the inherent tradeoff between the transparency and the piezoelectricity, numerous strategies have been explored to develop the transparent piezoelectric materials. Nonetheless, the most transparent piezoelectric materials developed to date is either a single crystal or materials that achieve transparency via hot-press sintering, limiting its industrial applicability. Therefore, we introduce a novel piezoelectric material that ensures transparency through co-doping and pressureless sintering of polycrystalline ceramics. In this study, we employed a compositional optimization approach to enhance the synergistic effect between the transparency and the piezoelectric properties of 0.71Pb(Mg1/3Nb2/3)O3–0.29PbTiO3 (PMN–0.29PT) ceramics. By utilizing the tape casting process for mass production and large-area manufacturing, our Pb0.913La0.0145Sm0.0145(Mg1/3Nb2/3)0.71Ti0.29O3 (TP2.9) ceramics exhibited over 60% transparency and large piezoelectric coefficient (d33) of 1104 pC/N. This material holds considerable promise for a wide range of industrial applications in both the optical and electronic domains.