Hasil untuk "Inorganic chemistry"

Marios Zacharias, George Volonakis, Laurent Pedesseau et al.

The role of data in modern materials science becomes more valuable and accurate when effects such as electron-phonon coupling and anharmonicity are included, providing a more realistic representation of finite-temperature material behavior. Furthermore, positional polymorphism, characterized by correlated local atomic disorder usually not reported by standard diffraction techniques, is a critical yet underexplored factor in understanding the electronic structure and transport properties of energy-efficient materials, like halide perovskites. In this manuscript, we present a first-principles methodology for locally disordered (polymorphous) cubic inorganic and hybrid halide perovskites, rooted in the special displacement method, that offers a systematic and alternative approach to molecular dynamics for exploring finite-temperature properties. By enabling a unified and efficient treatment of anharmonic lattice dynamics, electron-phonon coupling, and positional polymorphism, our approach generates essential data to predict temperature-dependent phonon properties, free energies, band gaps, and effective masses. Designed with a high-throughput spirit, this framework has been applied across a range of inorganic and hybrid halide perovskites: CsPbI3, CsPbBr3, CsSnI3, CsPbCl3, MAPbI3, MAPbBr3, MASnI3, MAPbCl3, FAPbI3, FAPbBr3, FASnI3, and FAPbCl3. We provide a comprehensive comparison between theoretical and experimental results and we systematically uncover trends and insights into their electronic and thermal behavior. For all compounds, we demonstrate strong and consistent correlations between local structural disorder, band gap openings, and effective mass enhancements.

Veronika Asova, Galin Bistrev, Valentin Buchakchiev et al.

Energy resolution and the detection efficiency for gamma quanta are fundamental properties in the construction of detectors for ionizing radiation. In this study, a SiPM-based photodetector coupled to a monolithic inorganic CeBr_{3} crystal is exposed to gamma rays in order to study the performance of the CeBr_{3} crystal. Measurements are made using three different radioactive sources - ^{137}Cs, ^{22}Na, and ^{60}Co. For each source, the measurements are conducted at several SiPM bias voltages. Furthermore, two CeBr_{3} crystals with different thicknesses are used in order to study how detector efficiency is affected by crystal dimensions. A preliminary analysis of the data is presented.

Xianrui Zhong, Bowen Jin, Siru Ouyang et al.

Retrieval-augmented generation (RAG) has emerged as a powerful framework for enhancing large language models (LLMs) with external knowledge, particularly in scientific domains that demand specialized and dynamic information. Despite its promise, the application of RAG in the chemistry domain remains underexplored, primarily due to the lack of high-quality, domain-specific corpora and well-curated evaluation benchmarks. In this work, we introduce ChemRAG-Bench, a comprehensive benchmark designed to systematically assess the effectiveness of RAG across a diverse set of chemistry-related tasks. The accompanying chemistry corpus integrates heterogeneous knowledge sources, including scientific literature, the PubChem database, PubMed abstracts, textbooks, and Wikipedia entries. In addition, we present ChemRAG-Toolkit, a modular and extensible RAG toolkit that supports five retrieval algorithms and eight LLMs. Using ChemRAG-Toolkit, we demonstrate that RAG yields a substantial performance gain -- achieving an average relative improvement of 17.4% over direct inference methods. We further conduct in-depth analyses on retriever architectures, corpus selection, and the number of retrieved passages, culminating in practical recommendations to guide future research and deployment of RAG systems in the chemistry domain. The code and data is available at https://chemrag.github.io.

Yueqing Zhang, Wentao Liu, Yan Zhang et al.

Large language models (LLM) have achieved impressive progress across a broad range of general-purpose tasks, but their effectiveness in chemistry remains limited due to scarce domain-specific datasets and the demand for precise symbolic and structural reasoning. Here we introduce ECNU-ChemGPT(name after East China Normal University), a chemistry-specialized LLM engineered for deep chemical knowledge understanding and accurate retrosynthetic route planning. Our approach is distinguished by four key strategies: structured prompt-based knowledge distillation from authoritative chemistry textbooks to construct a high-quality question-answering dataset; domain-specific prompt engineering using curated chemical keywords, combined with LLMs APIs for data derivation and knowledge distillation; large-scale fine-tuning on a meticulously cleaned and enriched Pistachio reaction dataset to enhance retrosynthesis prediction accuracy; and integration of BrainGPT, a dynamic multi-model scheduling framework that enables task-specific invocation of multiple specialized models trained for diverse chemistry-related tasks. ECNU-ChemGPT exhibits superior performance on chemistry question-answering and retrosynthetic planning benchmarks, outperforming leading general-purpose models-including Deepseek-R1, Qwen-2.5, and GPT-4o. In retrosynthesis, it achieves a Top-1 accuracy of 68.3% on the USPTO_50K dataset and successfully reconstructed 13 complete experimental pathways for real-world drug molecules from medicinal chemistry journals. These results underscore the effectiveness of domain-adapted fine-tuning combined with dynamic multi-model task scheduling, providing a scalable and robust solution for chemical knowledge question answering and retrosynthetic planning.

Zhiling Zheng

Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chem-istry course created for students on the synthetic chemistry track with no prior programming background. The curricu-lum emphasizes chemical context over abstract algorithms, using an accessible web-based platform to ensure zero-install machine learning (ML) workflow development practice and in-class active learning. Assessment combines code-guided homework, literature-based mini-reviews, and collaborative projects in which students build AI-assisted workflows for real experimental problems. Learning gains include increased confidence with Python, molecular property prediction, reaction optimization, and data mining, and improved skills in evaluating AI tools in chemistry. All course materials are openly available, offering a discipline-specific, beginner-accessible framework for integrating AI into synthetic chemistry training.

Dongyang Zhu, Ke Xu, Zhuangli Kang et al.

In this paper, the effects of TGase reaction times (0, 6, 12, 18, and 24 h) at 4 °C on the solubility, emulsion stability, cooking yield, gel properties and water distribution of reduced-fat and reduced-sodium chicken meat batter were studied. The results showed that the reaction time had a significant effect on the water fluidity and quality characteristics of reduced-fat and reduced-sodium chicken meat batter. The solubility, cooking yield and water-holding capacity of salt-soluble proteins initially increased then decreased with extended reaction time, reaching maximum values of 65.50%, 96.13% and 96.00%, respectively, at 12 h. The emulsifying stability and textural properties initially increased, then decreased with extended reaction time (<i>p</i> < 0.05), achieving optimal levels at 12 h. In contrast, the initial relaxation time of T21 and T22 initially decreased (<i>p</i> < 0.05) and then increased (<i>p</i> < 0.05) with longer reaction times; the minimum values were 12 h, especially the free water decreased from 17.97% to 6.69%, consistent with the finding on water-holding capacity and gel properties. In conclusion, the reaction time of the TGase affected its effect on improving the gel effect of reduced-fat and reduced-sodium chicken meat batter, and the best effect was achieved at 12 h.

Daniel K. Baines, Varvara Platania, Nikoleta N. Tavernaraki et al.

Bone-associated pathologies are major contributors to chronic pathology statistics. Current gold standard treatments present limitations such as the ability to act as scaffolds whilst effectively delivering medications to promote cellular proliferation. Recent advancements in biomaterials have suggested whey protein isolate (WPI) hydrogel as a potential candidate to act as a scaffold with the capacity for drug delivery for bone regeneration. In this study, we investigate whey protein isolate hydrogels enhanced with the phytocannabinoid cannabidiol (CBD). The use of CBD in WPI hydrogels for bone regeneration is original. The results suggest that CBD was successfully incorporated into the hydrogels bound potentially through hydrophobic interactions formed between hydrophobic patches of the protein and the hydrophobic cannabinoid. The incorporation of CBD into the WPI hydrogels improved the mechanical strength of the hydrogels. The Young’s modulus was improved from 2700 kPa ± 117 kPa to 7100 kPa ± 97 kPa when compared to the WPI control, without plant-derived cannabinoids, to the WPI with the maximum CBD concentration. Furthermore, statistically significant differences for both Young’s modulus and compressive strength were observable between the WPI control and CBD hydrogel variables. The release of CBD from the WPI hydrogels was confirmed with the results suggesting a maximum release of 20 μM over the 5-day period. Furthermore, the hydrogels supported the proliferation and synthesis of collagen and calcium, as well as the alkaline phosphatase activity of MC3T3-E1 pre-osteoblasts, which demonstrates the potential of WPI/CBD hydrogels as a biomaterial for osseous tissue regeneration.

Tomáš Musálek, Petr Liška, Amedeo Morsa et al.

Inorganic halide perovskites have become attractive for many optoelectronic applications due to their outstanding properties. While chemical synthesis techniques have been successful in producing high-quality perovskite crystals, scaling up to wafer-scale thin films remains challenging. Vapor deposition methods, particularly physical vapor deposition and chemical vapor deposition, have emerged as potential solutions for large-scale thin film fabrication. However, the control of phase purity during deposition remains problematic. Here, we investigate single-source (CsPbBr3) and dual-source (CsBr and PbBr2) vapor deposition techniques to achieve phase-pure CsPbBr3 thin films. Utilizing Knudsen Effusion Mass Spectrometry, we demonstrate that while the single-source CsPbBr3 evaporation is partially congruent, it leads to compositional changes in the evaporant over time. The dual-source evaporation, with a precise control of the PbBr2/CsBr flux ratio, can improve phase purity, particularly at elevated substrate temperatures at excess PbBr2 conditions. Our results give direct evidence that the growth is CsBr-limited. Overall, our findings provide critical insights into the vapor phase deposition processes, highlighting the importance of evaporation conditions in achieving the desired inorganic perovskite stoichiometry and morphology.

Md Mohaiminul Islam

In recent times, the use of machine learning in materials design and discovery has aided to accelerate the discovery of innovative materials with extraordinary properties, which otherwise would have been driven by a laborious and time-consuming trial-and-error process. In this study, a simple yet powerful fragment-based descriptor, Low Dimensional Fragment Descriptors (LDFD), is proposed to work in conjunction with machine learning models to predict important properties of a wide range of inorganic materials such as perovskite oxides, metal halide perovskites, alloys, semiconductor, and other materials system and can also be extended to work with interfaces. To predict properties, the generation of descriptors requires only the structural formula of the materials and, in presence of identical structure in the dataset, additional system properties as input. And the generation of descriptors involves few steps, encoding the formula in binary space and reduction of dimensionality, allowing easy implementation and prediction. To evaluate descriptor performance, six known datasets with up to eight components were compared. The method was applied to properties such as band gaps of perovskites and semiconductors, lattice constant of magnetic alloys, bulk/shear modulus of superhard alloys, critical temperature of superconductors, formation enthalpy and energy above hull convex of perovskite oxides. An advanced python-based data mining tool matminer was utilized for the collection of data. The prediction accuracies are equivalent to the quality of the training data and show comparable effectiveness as previous studies. This method should be extendable to any inorganic material systems which can be subdivided into layers or crystal structures with more than one atom site, and with the progress of data mining the performance should get better with larger and unbiased datasets.

Huiyan Li, Kaihang Sun, Shuxiang Xiong et al.

The development of highly effective bifunctional catalysts for n-hexadecane hydroisomerization is still essential to produce second-generation biodiesel. Herein, a Pt-Pd/ZSM-22-G (abbreviated as Pt-Pd/Z22-G) bimetallic catalyst was prepared by employing a room temperature electron reduction (RTER) method with glow discharge as the electron source. As a contrast, a series of Pt/Z22-H, Pd/Z22-H and Pt-Pd/Z22-H catalysts were prepared by the conventional hydrogen reduction method. The Pt-Pd/Z22-G catalyst reveals more exposed metal sites, larger CMe/CH+ values and an enhanced distribution of Pt-Pd(111) facets compared with the Pt/Z22-H, Pd/Z22-H and Pt-Pd/Z22-H catalysts. These modifications are originated from the stronger electron interactions and the smaller metal nanoparticles because of the effects of highly energetic reducing electrons. The n-hexadecane hydroisomerization results show that the iso-hexadecane yield over the Pt-Pd/Z22-G catalyst is 82.9%, which is the highest among four investigated catalysts in this work. This phenomenon occurs because more exposed Pt-Pd(111) facets and larger CMe/CH+ ratios are beneficial for the adsorption and hydrogenation of iso-alkene intermediates at metal sites to increase the iso-alkanes yield based on density functional theory (DFT) calculations. Furthermore, the iso-alkanes yield over the Pt-Pd/Z22-G catalyst also keeps steady after long-term tests for 120 h because of the limited metal aggregation and carbon deposition.

Krastena Nikolova, Margarita Velikova, Galia Gentscheva et al.

Practically all aboveground plants parts of <i>Passiflora</i> vines can be included in the compositions of dietary supplements, medicines, and cosmetics. It has a diverse chemical composition and a wide range of biologically active components that determine its diverse pharmacological properties. Studies related to the chemical composition of the plant are summarized here, and attention has been paid to various medical applications—(1) anti-inflammatory, nephroprotective; (2) anti-depressant; (3) antidiabetic; (4) hepatoprotective; (5) antibacterial and antifungal; and (6) antipyretic and other. This review includes studies on the safety, synergistic effects, and toxicity that may occur with the use of various dietary supplements based on it. Attention has been drawn to its application in cosmetics and to patented products containing passionflower.

Fateme Mahdikhany, Sean Driskill, Jeremy G. Philbrick et al.

One-dimensional (1D) van der Waals materials have emerged as an intriguing playground to explore novel electronic and optical effects. We report on inorganic one-dimensional SbPS4 nanotubes bundles obtained via mechanical exfoliation from bulk crystals. The ability to mechanically exfoliate SbPS4 nanobundles offers the possibility of applying modern 2D material fabrication techniques to create mixed-dimensional van der Waals heterostructures. We find that SbPS4 can readily be exfoliated to yield long (> 10 μm) nanobundles with thicknesses that range from of 1.3 - 200 nm. We investigated the optical response of semiconducting SbPS4 nanobundles and discovered that upon excitation with blue light, they emit bright and ultra-broadband red light with a quantum yield similar to that of hBN-encapsulated MoSe2. We discovered that the ultra-broadband red light emission is a result of a large ~1 eV exciton binding energy and a ~200 meV exciton self-trapping energy, unprecedented in previous material studies. Due to the bright and ultra-broadband light emission, we believe that this class of inorganic 1D van der Waals semiconductors has numerous potential applications including on-chip tunable nanolasers, and applications that require ultra-violet to visible light conversion such as lighting and sensing. Overall, our findings open avenues for harnessing the unique characteristics of these nanomaterials, advancing both fundamental research and practical optoelectronic applications.

Andreea Luca, Isabella Nacu, Sabina Tanasache et al.

The aim of the present work was to obtain drug-loaded hydrogels based on combinations of dextran, chitosan/gelatin/xanthan, and poly (acrylamide) as a sustained and controlled release vehicle of Doxorubicin, a drug used in skin cancer therapy that is associated with severe side effects. Hydrogels for use as 3D hydrophilic networks with good manipulation characteristics were produced using methacrylated biopolymer derivatives and the methacrylate group’s polymerization with synthetic monomers in the presence of a photo-initiator, under UV light stimulation (365 nm). Transformed infrared spectroscopy analysis (FT-IR) confirmed the hydrogels’ network structure (natural–synthetic composition and photocrosslinking), while scanning electron microscopy (SEM) analysis confirmed the microporous morphology. The hydrogels are swellable in simulated biological fluids and the material’s morphology regulates the swelling properties: the maximum swelling degree was obtained for dextran–chitosan-based hydrogels because of their higher porosity and pore distribution. The hydrogels are bioadhesive on a biological simulating membrane, and values for the force of detachment and work of adhesion are recommended for applications on skin tissue. The Doxorubicin was loaded into the hydrogels and the drug was released by diffusion for all the resulting hydrogels, with small contributions from the hydrogel networks’ relaxation. Doxorubicin-loaded hydrogels are efficient on keratinocytes tumor cells, the sustained released drug interrupting the cells’ division and inducing cell apoptosis; we recommend the obtained materials for the topical treatment of cutaneous squamous cell carcinoma.

Denise Bellotti, Maria D’Accolti, Walter Pula et al.

Calcitermin is an antimicrobial peptide of 15 amino acids found in human nasal fluid characterized by antifungal and antibacterial properties. <i>Candida albicans</i> is the most common human fungal pathogen affecting many tissues, such as vaginal mucosa. In this study a formulation suitable for calcitermin administration on vaginal mucosa was developed for the treatment of fungal infections. To favor topical application, mucosal adhesion, and permanence, gels based on poloxamer 407 and xanthan gum were designed and compared with regard to their rheological behavior, erosion, and leakage. The selected gel was loaded with calcitermin, whose release kinetic was evaluated in vitro by Franz cells. An antifungal activity assay was conducted to assess the calcitermin anticandidal potential and the effect of its inclusion in the selected gel. The rheological study revealed the elastic and viscous moduli behavior as a function of poloxamer 407 and xanthan gum concentration. Xanthan gum presence decreased the transition temperature of the gel, while prolonging its erosion and leakage. Particularly, poloxamer 407, 18% and xanthan gum 0.4% were chosen. The calcitermin loading in the selected gel resulted in a transparent and homogeneous formulation and in a 4-fold decrease of the release rate with respect to the calcitermin solution, as evidenced by Franz cell study. The anticandidal activity tests demonstrated that calcitermin-loaded gel was more active against <i>Candida albicans</i> with respect to the peptide solution.

Alevtina Semkina, Aleksey Nikitin, Anna Ivanova et al.

Magnetic nanoparticles based on iron oxide attract researchers’ attention due to a wide range of possible applications in biomedicine. As synthesized, most of the magnetic nanoparticles do not form the stable colloidal solutions that are required for the evaluation of their interactions with cells or their efficacy on animal models. For further application in biomedicine, magnetic nanoparticles must be further modified with biocompatible coating. Both the size and shape of magnetic nanoparticles and the chemical composition of the coating have an effect on magnetic nanoparticles’ interactions with living objects. Thus, a universal method for magnetic nanoparticles’ stabilization in water solutions is needed, regardless of how magnetic nanoparticles were initially synthesized. In this paper, we propose the versatile and highly reproducible ligand exchange technique of coating with 3,4-dihydroxiphenylacetic acid (DOPAC), based on the formation of Fe-O bonds with hydroxyl groups of DOPAC leading to the hydrophilization of the magnetic nanoparticles’ surfaces following phase transfer from organic solutions to water. The proposed technique allows for obtaining stable water–colloidal solutions of magnetic nanoparticles with sizes from 21 to 307 nm synthesized by thermal decomposition or coprecipitation techniques. Those stabilized by DOPAC nanoparticles were shown to be efficient in the magnetomechanical actuation of DNA duplexes, drug delivery of doxorubicin to cancer cells, and targeted delivery by conjugation with antibodies. Moreover, the diversity of possible biomedical applications of the resulting nanoparticles was presented. This finding is important in terms of nanoparticle design for various biomedical applications and will reduce nanomedicines manufacturing time, along with difficulties related to comparative studies of magnetic nanoparticles with different magnetic core characteristics.