Hasil untuk "Physical and theoretical chemistry"

M. Senge, Marijana Fazekas, Eleni G. A. Notaras et al.

Yu Feng, Jiagang Wu, Q. Chi et al.

Since the positive influences of defects on the performance of electroceramics were discovered, investigations concerning on defects and aliovalent doping routes have grown rapidly in the fields of inorganic chemistry and condensed matter physics. In this article, we summarized the types of defects in electroceramics as well as characterization tools of defects and highlighted the effects of intrinsic and extrinsic defects on the material performances with the emphasis on dielectric, ferroelectric, and piezoelectric properties. We mainly introduced defect related theoretical simulation and experimental results in several typical incipient ferroelectrics, ferroelectrics, and antiferroelectrics. Hence, the influences of defects on the crystal lattice were summed up, and then the main physical mechanisms were highlighted. Particularly, the performance enhancements of aliovalently doped electroceramics were also evaluated and reviewed. Finally, the outlook and challenges were discussed on the basis of their current developments. This article covers not only an overview of the state-of-the-art advances of defects and aliovalent doping routes in electroceramics but also the future prospects that may open another window to tune the electrical performance of electroceramics via intentionally introducing certain defects.

A. Dokoumetzidis, P. Macheras

Tao E. Li, B. Cui, Joseph E. Subotnik et al.

Chemical manifestations of strong light-matter coupling have recently been a subject of intense experimental and theoretical studies. Here we review the present status of this field. Section 1 is an introduction to molecular polaritonics and to collective response aspects of light-matter interactions. Section 2 provides an overview of the key experimental observations of these effects, while Section 3 describes our current theoretical understanding of the effect of strong light-matter coupling on chemical dynamics. A brief outline of applications to energy conversion processes is given in Section 4. Pending technical issues in the construction of theoretical approaches are briefly described in Section 5. Finally, the summary in Section 6 outlines the paths ahead in this exciting endeavor. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

D. Knopf, P. Alpert, Bingbing Wang

C. Marian

This review article focuses on the understanding of intersystem crossing (ISC) in molecules. It addresses readers who are interested in the phenomenon of intercombination transitions between states of different electron spin multiplicities but are not familiar with relativistic quantum chemistry. Among the spin-dependent interaction terms that enable a crossover between states of different electron spin multiplicities, spin-orbit coupling (SOC) is by far the most important. If SOC is small or vanishes by symmetry, ISC can proceed by electronic spin-spin coupling (SSC) or hyperfine interaction (HFI). Although this review discusses SSC- and HFI-based ISC, the emphasis is on SOC-based ISC. In addition to laying the theoretical foundations for the understanding of ISC, the review elaborates on the qualitative rules for estimating transition probabilities. Research on the mechanisms of ISC has experienced a major revival in recent years owing to its importance in organic light-emitting diodes (OLEDs). Exemplified by challenging case studies, chemical substitution and solvent environment effects are discussed with the aim of helping the reader to understand and thereby get a handle on the factors that steer the efficiency of ISC. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 72 is April 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

M. Becker, P. Loche, M. Rezaei et al.

From the stability of colloidal suspensions to the charging of electrodes, electric double layers play a pivotal role in aqueous systems. The interactions between interfaces, water molecules, ions and other solutes making up the electrical double layer span length scales from Ångströms to micrometers and are notoriously complex. Therefore, explaining experimental observations in terms of the double layer’s molecular structure has been a long-standing challenge in physical chemistry, yet recent advances in simulations techniques and computational power have led to tremendous progress. In particular, the past decades have seen the development of a multiscale theoretical framework based on the combination of quantum density functional theory, force-field based simulations and continuum theory. In this Review, we discuss these theoretical developments and make quantitative comparisons to experimental results from, among other techniques, sum-frequency generation, atomic-force microscopy, and electrokinetics. Starting from the vapor/water interface, we treat a range of qualitatively different types of surfaces, varying from soft to solid, from hydrophilic to hydrophobic, and from charged to uncharged.

Vinicius S. Nunes, Charles N. Serhan, Odonírio Abrahão et al.

Samuel Wohl, Yishai Gilron, Wenwei Zheng

Sotiris Konstantinou-Rizos

We present a method for constructing hierarchies of solutions to $n$-simplex equations by variating the spectral parameter in their Lax representation. We use this method to derive new solutions to the set-theoretical 2- and 3-simplex equations which are related to the Adler map and Nonlinear Schrödinger (NLS) type equations. Moreover, we prove that some of the derived Yang--Baxter maps are completely integrable.

Kefeng Huang, Jonathon Pipe, Alice E. Martin et al.

Chemistry laboratory automation aims to increase throughput, reproducibility, and safety, yet many existing systems still depend on frequent human intervention. Advances in robotics have reduced this dependency, but without a structured representation of the required skills, autonomy remains limited to bespoke, task-specific solutions with little capacity to transfer beyond their initial design. Current experiment abstractions typically describe protocol-level steps without specifying the robotic actions needed to execute them. This highlights the lack of a systematic account of the manipulation skills required for robots in chemistry laboratories. To address this gap, we introduce TARMAC - a Taxonomy for Robot Manipulation in Chemistry - a domain-specific framework that defines and organizes the core manipulations needed in laboratory practice. Based on annotated teaching-lab demonstrations and supported by experimental validation, TARMAC categorizes actions according to their functional role and physical execution requirements. Beyond serving as a descriptive vocabulary, TARMAC can be instantiated as robot-executable primitives and composed into higher-level macros, enabling skill reuse and supporting scalable integration into long-horizon workflows. These contributions provide a structured foundation for more flexible and autonomous laboratory automation. More information is available at https://tarmac-paper.github.io/

Sirui Lu, Zhijing Jin, Terry Jingchen Zhang et al.

Large Language Models (LLMs) are rapidly advancing across diverse domains, yet their application in theoretical physics remains inadequate. While current models show competence in mathematical reasoning and code generation, we identify critical gaps in physical intuition, constraint satisfaction, and reliable reasoning that cannot be addressed through prompting alone. Physics demands approximation judgment, symmetry exploitation, and physical grounding that require AI agents specifically trained on physics reasoning patterns and equipped with physics-aware verification tools. We argue that LLM would require such domain-specialized training and tooling to be useful in real-world for physics research. We envision physics-specialized AI agents that seamlessly handle multimodal data, propose physically consistent hypotheses, and autonomously verify theoretical results. Realizing this vision requires developing physics-specific training datasets, reward signals that capture physical reasoning quality, and verification frameworks encoding fundamental principles. We call for collaborative efforts between physics and AI communities to build the specialized infrastructure necessary for AI-driven scientific discovery.

Briley L. Lewis

Writing is a critical skill for modern science, enabling collaboration, scientific discourse, public outreach, and more. Accordingly, it is important to consider how physicists and astronomers are trained to write. This study aims to understand the landscape of science writing education, specifically in physics and astronomy, in higher education in the United States. An online survey probing various aspects of their writing training in both undergraduate and graduate school was administered to 515 participants who have obtained training in physics and/or astronomy, or related fields, at the level equal to or beyond upper-division undergraduate study. Humanities and writing requirement courses appear to have a key role in general writing education, while laboratory courses and feedback from mentors are the dominant modes of science writing education in undergraduate and graduate school respectively. There is substantial variation in the quality of writing education in physics and astronomy, often dependent on the student's institution and/or mentor. Some participants also report that their success in disciplinary writing was a result of a solid foundation from K-12 education and/or self-direction towards resources; such reliance on past experiences and student background may contribute to inequality in the field. Many participants also stated a clear desire for more structured writing training to be available in the field. We provide suggestions for how to implement such training to meet the needs of the community identified in the survey.

Shahid Zaman, Mehwish Jalani, Asad Ullah et al.

Metal–Organic Networks (MONs) are made by chemical molecules that contain metal ions and organic ligands. A crystalline porous solid called Metal–Organic Networks (MONs) is made up of a $$3D$$ 3 D metal network of ions held in place by a multidentate ligand. (MONs) can be used for gas storage, purification drug delivery, gas separation, catalysis, and sensing applications. There is enormous potential for effective integration and research of MONs in diverse applications. Molecular descriptors are arithmetic measures that reveal a chemical substance's physical and chemical characteristics in its foundational network in a natural relationship. They demonstrate an important role in theoretical and ecological chemistry, and in the field of medicine. In this research, we calculated various recently discovered molecular descriptors viz. the modified version of second zagreb index, harmonic index, reciprocal randic index, modified version of forgotten topological index, redefined first zagreb topological index, redefined second zagreb topological index and redefined third zagreb topological index for two separate metal–organic networks. The numerical and graphical comparative analysis of these considered molecular descriptors are also performed.

T. J. Millar, C. Walsh, M. Sande et al.

Detailed astrochemical models are a key component to interpret the observations of interstellar and circumstellar molecules since they allow important physical properties of the gas and its evolutionary history to be deduced. We update one of the most widely used astrochemical databases to reflect advances in experimental and theoretical estimates of rate coefficients and to respond to the large increase in the number of molecules detected in space since our last release in 2013. We present the sixth release of the UMIST Database for Astrochemistry (UDfA), a major expansion of the gas-phase chemistry that describes the synthesis of interstellar and circumstellar molecules. Since our last release, we have undertaken a major review of the literature which has increased the number of reactions by over 40% to a total of 8767 and increased the number of species by over 55% to 737. We have made a particular attempt to include many of the new species detected in space over the past decade, including those from the QUIJOTE and GOTHAM surveys, as well as providing references to the original data sources. We use the database to investigate the gas-phase chemistries appropriate to O-rich and C-rich conditions in TMC-1 and to the circumstellar envelope of the C-rich AGB star IRC+10216 and identify successes and failures of gas-phase only models. This update is a significant improvement to the UDfA database. For the dark cloud and C-rich circumstellar envelope models, calculations match around 60% of the abundances of observed species to within an order of magnitude. There are a number of detected species, however, that are not included in the model either because their gas-phase chemistry is unknown or because they are likely formed via surface reactions on icy grains. Future laboratory and theoretical work is needed to include such species in reaction networks.

Didjour Albert Kambiré, Kayatou Touré, Thierry Acafou Yapi et al.

This study aims at investigating the chemical composition of root, stem and leaf essential oils from Ivorian <i>Synedrella nodiflora</i>, with the root oil being described for the first time. Sixty, fifty-one and forty-nine constituents were, respectively identified in the root, stem and leaf oils using a combination of GC(RI), GC-MS and ¹³C-NMR analyses. They accounted for 95.6–97.3%, 92.6–97.6% and 93.3–98.8% of the total composition, respectively. The main components of the root oil samples were γ-curcumene, (<i>E</i>)-β-caryophyllene, α-curcumene and curcuphenyl acetate. Three stem oil samples (S1, S2a, S3) were dominated by myrcene and limonene, while the most abundant components of sample S2b were thymol, germacrene D and β-elemene. (<i>E</i>)-β-caryophyllene and germacrene D were the major compounds of the leaf oil. Hierarchical cluster and principal component statistical analyses were performed and confirmed that the location does not influence the chemical composition. Group I consisted of the seven leaf oil samples, group II consisted of four stem oil samples and group III consisted of three root oil samples. The root oil composition differed considerably from the stem and leaf oil composition due to the presence of curcumene derivatives as major constituents. The leaf oil showed significant amounts of (<i>E</i>)-β-caryophyllene and germacrene D, while the stem oil stood out for its high myrcene, limonene and thymol contents.

Chen Zhang, Valerie A. Niemann, Peter Benedek et al.

Ren Yanjie, Zhang Yun, Li Xiaobing

Silver nanoparticles (AgNPs) can provide excellent, reliable, and effective solutions for anti-microbial, drug-loading, and other purposes due to their extraordinary physical, chemical, and biological characteristics. Different methods have been used in the synthesis and characterization of AgNPs, and AgNPs have been applied in various fields of biomedicine, including dentistry, oncology, diabetology, neurodegenerative disorders, and so on. However, the cytotoxicity of AgNPs has not been solved during their application, making them controversial. The aim of this review is to summarize the capabilities, synthesis, and characterization methods, and the application of AgNPs in various biomedicine fields. In addition, the toxicity of AgNPs is explicated, and the methods of enhancing the benefit properties and reducing the toxicity of AgNPs are demonstrated. In the end, the perspective of AgNPs’ research and application are proposed for the great potential in biomedicine contributing to human health.

Kengo HAMASAKI, Ryoichi TOMIYAMA, Shin YONEYAMA et al.

It was found that diphenylacetylene was synthesized by using electrochemical reduction with Mg electrodes, coiled with Cu wires. The electrochemical reaction of tetrachloroethylene and bromobenzene in LiClO4/THF afforded diphenylacetylene in up to 38 % yield. The reaction took place in the absence of Pd catalyst. The scope and limitations, and investigation of reaction mechanism were also studied.

Yang Han, Ziping Wan, Lu Chen et al.

Large Language Models (LLMs) have significantly transformed our daily life and established a new paradigm in natural language processing (NLP). However, the predominant pretraining of LLMs on extensive web-based texts remains insufficient for advanced scientific discovery, particularly in chemistry. The scarcity of specialized chemistry data, coupled with the complexity of multi-modal data such as 2D graph, 3D structure and spectrum, present distinct challenges. Although several studies have reviewed Pretrained Language Models (PLMs) in chemistry, there is a conspicuous absence of a systematic survey specifically focused on chemistry-oriented LLMs. In this paper, we outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs, we also conceptualize chemistry LLMs as agents using chemistry tools and investigate their potential to accelerate scientific research. Additionally, we conclude the existing benchmarks to evaluate chemistry ability of LLMs. Finally, we critically examine the current challenges and identify promising directions for future research. Through this comprehensive survey, we aim to assist researchers in staying at the forefront of developments in chemistry LLMs and to inspire innovative applications in the field.