Hasil untuk "Chemical industries"

S. Brul, P. Coote

W. Saenger

B. Geffroy, P. L. Roy, C. Prat

R. Datta, M. Henry

D. Chandler, Alastair Bailey, G. Tatchell et al.

M. Ayoub, A. Abdullah

P. M. de Souza, Pérola de Oliveira Magalhães

Amylases are one of the main enzymes used in industry. Such enzymes hydrolyze the starch molecules into polymers composed of glucose units. Amylases have potential application in a wide number of industrial processes such as food, fermentation and pharmaceutical industries. α-Amylases can be obtained from plants, animals and microorganisms. However, enzymes from fungal and bacterial sources have dominated applications in industrial sectors. The production of α-amylase is essential for conversion of starches into oligosaccharides. Starch is an important constituent of the human diet and is a major storage product of many economically important crops such as wheat, rice, maize, tapioca, and potato. Starch-converting enzymes are used in the production of maltodextrin, modified starches, or glucose and fructose syrups. A large number of microbial α-amylases has applications in different industrial sectors such as food, textile, paper and detergent industries. The production of α-amylases has generally been carried out using submerged fermentation, but solid state fermentation systems appear as a promising technology. The properties of each α-amylase such as thermostability, pH profile, pH stability, and Ca-independency are important in the development of fermentation process. This review focuses on the production of bacterial and fungal α-amylases, their distribution, structural-functional aspects, physical and chemical parameters, and the use of these enzymes in industrial applications.

Stephen A Kelly, Stefan Pohle, S. Wharry et al.

Tony O Hara, Baljit Singh

The release of chemicals into water systems has resulted in pollution in many parts of the world, threatening human health and aquatic ecosystems. Sources of chemical discharge include industry, ag...

Marcia Leyva Salas, J. Mounier, F. Valence et al.

Food spoilage is a major issue for the food industry, leading to food waste, substantial economic losses for manufacturers and consumers, and a negative impact on brand names. Among causes, fungal contamination can be encountered at various stages of the food chain (e.g., post-harvest, during processing or storage). Fungal development leads to food sensory defects varying from visual deterioration to noticeable odor, flavor, or texture changes but can also have negative health impacts via mycotoxin production by some molds. In order to avoid microbial spoilage and thus extend product shelf life, different treatments—including fungicides and chemical preservatives—are used. In parallel, public authorities encourage the food industry to limit the use of these chemical compounds and develop natural methods for food preservation. This is accompanied by a strong societal demand for ‘clean label’ food products, as consumers are looking for more natural, less severely processed and safer products. In this context, microbial agents corresponding to bioprotective cultures, fermentates, culture-free supernatant or purified molecules, exhibiting antifungal activities represent a growing interest as an alternative to chemical preservation. This review presents the main fungal spoilers encountered in food products, the antifungal microorganisms tested for food bioprotection, and their mechanisms of action. A focus is made in particular on the recent in situ studies and the constraints associated with the use of antifungal microbial agents for food biopreservation.

Lipeng Wu, Takahiko Moteki, A. Gokhale et al.

Xin Liu, Mingxiang Chen, Jiuzhou Zhao et al.

In the development of static luminescent materials with remarkable optical-thermal performance and low cost, next-generation high-brightness laser lighting faces a key challenge. Herein, a unique composite architecture of Y3Al5O12:Ce3+ (YAG) phosphor-in-glass film coated on different heat-conducting substrates (PiGF@HCSs), i.e., PiGF@sapphire, PiGF@Al2O3, PiGF@AlN, and PiGF@BN–AlN composites, was designed and prepared by a simple film printing and low-temperature sintering technology. The heat-conducting substrates significantly affect the luminescence saturation and phosphor conversion of PiGF@HCSs, allowing substrates with higher thermal conductivity (TC) to have a higher laser power density (LPD) and higher reflectivity to enable higher luminous efficacy (LE). As a consequence, PiGF@sapphire realizes a luminous flux (LF) of 2076 lm@12 W/mm2, which is higher than those of PiGF@Al2O3 (1890 lm@15 W/mm2) and PiGF@AlN (1915 lm@24 W/mm2), whilePiGF@BN–AlN enables a maximum LF of 3058 lm@21 W/mm2. Furthermore, the LE of PiGF@BN–AlN reaches 194 lm/W, which is 1.6 times that of PiGF@AlN, while those of PiGF@sapphire and PiGF@Al2O3 are 192 and 150 lm/W, respectively. The working temperature of PiGF@AlN is only 93.3 °C under LPD of 9 W/mm2, while those of PiGF@sapphire, PiGF@Al2O3, and PiGF@BN–AlN increase to 193.8, 133.6, and 117 °C, respectively. These findings provide guidance for commercial applications of PiGF@HCS converters in high-brightness laser lighting and displays.

Liu Jiahang, Wang Yiyong, Lu Zhe et al.

The outstanding thermophysical properties and mechanical properties are crucial for the application of RE2Zr2O7 in thermal barrier coatings (TBCs). To simultaneously optimize the thermal conductivity, thermal expansion behaviour and mechanical properties of rare-earth zirconate ceramics, in this work a novel highentropy (Sc0.2La0.2Sm0.2Er0.2Yb0.2)2Zr2O7 (REZO) ceramics was designed with significant mass and size differences based on the thermal properties tailoring theory. Structural analysis revealed that the REZO ceramics prepared by conventional solid-state reaction exhibits a dual-phase structure with coexisting pyrochlore and fluorite phases, and the five rare-earth cations were uniformly distributed throughout REZO without compositional segregation. In terms of thermophysical properties, compared to La2Zr2O7 and Gd2Zr2O7, the REZO exhibits a glass-like thermal conductivity (1.31˙W•m−1•K−1, at room temperature) and a high thermal expansion coefficient (11.054 × 10−6/K, 1200°C). Additionally, the REZO demonstrates excellent high-temperature phase stability from room temperature to 1600°C. In terms of mechanical properties, the REZO exhibits a lower Young’s modulus, higher Vickers hardness and higher fracture toughness compared to La2Zr2O7 and Gd2Zr2O7. In summary, the thermal properties tailoring theory employed in this work provides a novel design approach for developing RE2Zr2O7 ceramics with tunable thermophysical and mechanical properties, enhancing the application prospects of RE2Zr2O7 in advanced TBCs.

Supratim Ghosh, Nupur Jain, Raghavan B. Sunoj

Developing machine learning (ML) models for yield prediction of chemical reactions has emerged as an important use case scenario in very recent years. In this space, reaction datasets present a range of challenges mostly stemming from imbalance and sparsity. Herein, we consider chemical language representations for reactions to tap into the potential of natural language processing models such as the ULMFiT (Universal Language Model Fine Tuning) for yield prediction, which is customized to work across such distribution settings. We contribute a new reaction dataset with more than 860 manually curated reactions collected from literature spanning over a decade, belonging to a family of catalytic meta-C(sp2)-H bond activation reactions of high contemporary importance. Taking cognizance of the dataset size, skewness toward the higher yields, and the sparse distribution characteristics, we developed a new (i) time- and resource-efficient pre-training strategy for downstream transfer learning, and (ii) the CFR (classification followed by regression) model that offers state-of-the-art yield predictions, surpassing conventional direct regression (DR) approaches. Instead of the prevailing pre-training practice of using a large number of unlabeled molecules (1.4 million) from the ChEMBL dataset, we first created a pre-training dataset SSP1 (0.11 million), by using a substructure-based mining from the PubChem database, which is found to be equally effective and more time-efficient in offering enhanced performance. The CFR model with the ULMFiT-SSP1 regressor achieved an impressive RMSE of 8.40 for the CFR-major and 6.48 for the CFR-minor class in yield prediction on the title reaction, with a class boundary of yield at 53 %. Furthermore, the CFR model is highly generalizable as evidenced by the significant improvement over the previous benchmark reaction datasets.

Andrew J. Medford, Todd N. Whittaker, Bjarne Kreitz et al.

Artificial intelligence (AI) is influencing heterogeneous catalysis research by accelerating simulations and materials discovery. A key frontier is integrating AI with multiscale models and multimodal experiments to address the "many-to-one" challenge of linking intrinsic kinetics to observables. Advances in machine-learned force fields, microkinetics, and reactor modeling enable rapid exploration of chemical spaces, while operando and transient data provide unprecedented insight. Yet, inconsistent data quality and model complexity limit mechanistic discovery. Generative and agentic AI can automate model generation, quantify uncertainty, and couple theory with experiment, realizing "self-driving models" that produce interpretable, reproducible, and transferable understanding of catalytic systems.

Andrew Ma, Owen Dugan, Marin Soljačić

In solid-state materials science, substantial efforts have been devoted to the calculation and modeling of the electronic band gap. While a wide range of ab initio methods and machine learning algorithms have been created that can predict this quantity, the development of new computational approaches for studying the band gap remains an active area of research. Here we introduce a simple machine learning model for predicting the band gap using only the chemical composition of the crystalline material. To motivate the form of the model, we first analyze the empirical distribution of the band gap, which sheds new light on its atypical statistics. Specifically, our analysis enables us to frame band gap prediction as a task of modeling a mixed random variable, and we design our model accordingly. Our model formulation incorporates thematic ideas from chemical heuristic models for other material properties in a manner that is suited towards the band gap modeling task. The model has exactly one parameter corresponding to each element, which is fit using data. To predict the band gap for a given material, the model computes a weighted average of the parameters associated with its constituent elements and then takes the maximum of this quantity and zero. The model provides heuristic chemical interpretability by intuitively capturing the associations between the band gap and individual chemical elements.

Prerna Paliwal, Jutta Toscano, Stefan Willitsch

Over the past years, radiofrequency ion traps have become an attractive platform for studying chemical reactions as they enable a high degree of control over ion-molecule dynamics. In this review, we summarize techniques for the trapping and cooling of atomic and molecular ions in radiofrequency traps including Doppler and resolved-sideband laser cooling, sympathetic cooling, and cryogenic buffer-gas methods. We discuss strategies for controlling key reaction parameters: the preparation of specific internal quantum states by internal cooling, optical pumping, state-selective photoionization and quantum-logic spectroscopy; the manipulation of collision energies through micromotion control, dynamic trapping and combination with molecular beams; and the selection of molecular structure via isotopic substitution, conformational separation and isomer-specific ion generation. We illustrate applications of these approaches by discussing studies on quantum-state-dependent kinetics, quantum-resonance effects and structure-sensitive reactivity in ion-neutral collisions. We conclude by outlining future challenges, including full state-to-state reaction mapping, reaching the ultracold quantum regime free of micromotion, and the exploration of complex and chiral systems.

Yaling Ke

In this work, we investigate the influence of light-matter coupling on reaction dynamics and equilibrium properties of a single molecule inside an optical cavity. The reactive molecule is modeled using a triple-well potential, allowing two competing reaction pathways that yield distinct products. Dynamical and equilibrium simulations are performed using the numerically exact hierarchical equations of motion approach in real- and imaginary-time formulations, respectively, both implemented with tree tensor network decomposition schemes. We consider two illustrative cases: one dominated by slow kinetics and another by ultrafast processes. Our results demonstrate that the rates of ground-state reaction pathways can be selectively enhanced when the cavity frequency is tuned into resonance with a vibrational transition directly leading to the formation of the corresponding product, even when that transition is spectroscopically dark. However, tuning cavity frequency to match an absorption-dominant transition shared across both reaction pathways does not necessarily result in pronounced rate enhancements and selectivity. Together with an additional analysis using an asymmetric double-well model, we highlight the greater complexity of underlying factors governing chemical reactivity, which extend beyond considerations of transition dipole strengths and thermal population distributions that shape linear spectroscopy. Furthermore, we found that in all scenarios, the equilibrium populations remain unchanged when the molecule is moved into the cavity, regardless of the cavity frequency. Thus, our study confirms at a fully quantum-mechanical level that cavity-induced modifications of chemical reactivities in resonant conditions arise from dynamical and non-equilibrium interactions between the cavity mode and molecular vibrations, rather than from the significant changes in equilibrium properties.

Jae Hee Jeong, Sunhyun Park, Mi Jang et al.

<i>Vibrio cholerae</i> and <i>Vibrio parahaemolyticus</i> are common pathogens linked to human gastroenteritis, particularly in seafood like shrimp. This study investigated the impact of lactic acid bacteria on <i>V. cholerae</i> and <i>V. parahaemolyticus</i> regarding the production of cadaverine, a concerning compound. <i>V. cholerae</i> NCCP 13589 and <i>V. parahaemolyticus</i> ATCC 27969 were significant producers of amines in experiments conducted using white-leg shrimp (<i>Litopenaeus vannamei</i>) and lysine decarboxylase broth. Notably, the <i>Lactiplantibacillus plantarum</i> NCIMB 6105 and <i>Leuconostoc mesenteroides</i> ATCC 10830 lactic acid bacteria strains demonstrated a pronounced antagonistic effect on the production of biogenic amines by these food-borne pathogenic bacteria. The presence of lactic acid bacteria led to a substantial reduction in cadaverine production in the lysine decarboxylase broth and shrimp extract. The co-culture of two lactobacilli species reduced the cadaverine production in <i>V. cholerae</i> and <i>V. parahaemolyticus</i> by approximately 77 and 80%, respectively. Consequently, the favorable influence of lactic acid bacteria in curbing cadaverine production by food-borne pathogens presents clear advantages for the food industry. Thus, effectively managing these pathogens could prove pivotal in controlling the biogenic amine levels in shrimp.

Nils Göth, Joachim Dzubiella

Chemical communication, response, and feedback are key requirements for the function of adaptive materials with life-like properties. However, how communication on the single cell-level impacts the collective structural, dynamical and mechanical behavior of active soft matter is not well understood. Here, we report how communication controls the spatiotemporal structure and phase behavior of active, hydrogel-based colloidal liquids using Brownian particle-based simulations with explicit resolution of the chemical signaling waves as well as the individual particle's elastic response to communication and crowding. We find a rich topology of nonequilibrium active phases, vastly tuneable by the signaling magnitude, in particular, active melting, synchronization transitions from uncorrelated to antiphase oscillatory liquids, or to in-phase oscillations with accompanying elasto-chemical cluster waves. Our work employs minimal physical principles required for communication-mediated dynamics of microscopic, fluctuating systems, thus uncovering universal aspects in signaling soft systems.