Hasil untuk "Organic chemistry"

B. Theng

M. H. Shaw, Jack Twilton, D. MacMillan

In recent years, photoredox catalysis has come to the forefront in organic chemistry as a powerful strategy for the activation of small molecules. In a general sense, these approaches rely on the ability of metal complexes and organic dyes to convert visible light into chemical energy by engaging in single-electron transfer with organic substrates, thereby generating reactive intermediates. In this Perspective, we highlight the unique ability of photoredox catalysis to expedite the development of completely new reaction mechanisms, with particular emphasis placed on multicatalytic strategies that enable the construction of challenging carbon–carbon and carbon–heteroatom bonds.

H. Furukawa, K. E. Cordova, M. O'Keeffe et al.

R. Schwarzenbach, P. Gschwend, D. Imboden

C. Reichardt

Alex J. Roche

A. Vogel, B. Furniss

J. March

K. Moeller

Steven J Lyle, Peter J. Waller, O. Yaghi

Covalent organic frameworks are constructed by covalently linking organic molecules into crystalline 2D and 3D networks. Their architectural and chemical stability, coupled with their porosity, has allowed them to be used as starting materials and products of molecular organic reactions. Increasingly sophisticated structural and chemical design strategies have enabled the synthesis of complex 3D, 2D, and 1D (weaving) structures from geometrically predefined building blocks. Like small molecules, these materials allow for precise spatial organization of chemical functionalities but do so at length scales ranging from a few angstroms to several microns.

A. Filipa de Almeida, R. Moreira, T. Rodrigues

Maria Angelova, Petko Alov, Ivanka Tsakovska et al.

Scoring functions are key elements in docking protocols as they approximate the binding affinity of a ligand (usually a small bioactive molecule) by calculating its interaction energy with a biomacromolecule (usually a protein). In this study, we present a pairwise comparison of scoring functions applying a multi-criterion decision-making approach based on InterCriteria analysis (ICrA). As criteria, the five scoring functions implemented in MOE (Molecular Operating Environment) software were selected, and their performance on a set of protein–ligand complexes from the PDBbind database was compared. The following docking outputs were used: the best docking score, the lowest root mean square deviation (RMSD) between the predicted poses and the co-crystallized ligand, the RMSD between the best docking score pose and the co-crystallized ligand, and the docking score of the pose with the lowest RMSD to the co-crystallized ligand. The impact of ICrA thresholds on the relations between the scoring functions was investigated. A correlation analysis was also performed and juxtaposed with the ICrA. Our results reveal the lowest RMSD as the best-performing docking output and two scoring functions (Alpha HB and London dG) as having the highest comparability. The proposed approach can be applied to any other scoring functions and protein–ligand complexes of interest.

A. Aliane, A. Daami, T. Card et al.

A huge interesting progress in the field of organic electronic materials and devices has been observed in the last decade. However, the understanding of these materials is still a challenge to overcome. Most studies in literature focus on active devices such as OTFTs, OLEDs and OPVs. Nevertheless, a complete technology has to have also passive devices in order to allow the design of interesting applications and complex circuits. This paper deals with the development of a complete set of passive devices allowing the fabrication of simple applications such as filters or sensors. The process flow is a fully screen printed technology that uses exclusively organic materials on gold laser ablated flexible substrate. Discrete passive (R, L, C) devices have been processed and characterized. This has permitted the fabrication of RLC low-band pass filters that are dedicated to RF applications, typically around 1GHz. Furthermore, based on these discrete passive components, we have developed a sensitive sensor on flexible substrate for RFID applications. We present the state of the art of our process development for RF applications using organic materials.

Yiming Cui, Xin Yao, Yuxuan Qin et al.

Multimodal scientific reasoning remains a significant challenge for large language models (LLMs), particularly in chemistry, where problem-solving relies on symbolic diagrams, molecular structures, and structured visual data. Here, we systematically evaluate 40 proprietary and open-source multimodal LLMs, including GPT-5, o3, Gemini-2.5-Pro, and Qwen2.5-VL, on a curated benchmark of Olympiad-style chemistry questions drawn from over two decades of U.S. National Chemistry Olympiad (USNCO) exams. These questions require integrated visual and textual reasoning across diverse modalities. We find that many models struggle with modality fusion, where in some cases, removing the image even improves accuracy, indicating misalignment in vision-language integration. Chain-of-Thought prompting consistently enhances both accuracy and visual grounding, as demonstrated through ablation studies and occlusion-based interpretability. Our results reveal critical limitations in the scientific reasoning abilities of current MLLMs, providing actionable strategies for developing more robust and interpretable multimodal systems in chemistry. This work provides a timely benchmark for measuring progress in domain-specific multimodal AI and underscores the need for further advances at the intersection of artificial intelligence and scientific reasoning.

M. Cortes‐Clerget, Julie Yu, Joseph R. A. Kincaid et al.

A review presenting water as the logical reaction medium for the future of organic chemistry. A discussion is offered that covers both the “on water” and “in water” phenomena, and how water is playing unique roles in each, specifically with regard to its use in organic synthesis.

Mathijs F. J. Mabesoone, A. Palmans, E. Meijer

Interactions between solvents and solutes are a cornerstone of physical organic chemistry and have been the subject of investigations over the last century. In recent years, a renewed interest in fundamental aspects of solute–solvent interactions has been sparked in the field of supramolecular chemistry in general and that of supramolecular polymers in particular. Although solvent effects in supramolecular chemistry have been recognized for a long time, the unique opportunities that supramolecular polymers offer to gain insight into solute–solvent interactions have become clear relatively recently. The multiple interactions that hold the supramolecular polymeric structure together are similar in strength to those between solute and solvent. The cooperativity found in ordered supramolecular polymers leads to the possibility of amplifying these solute–solvent effects and will shed light on extremely subtle solvation phenomena. As a result, many exciting effects of solute–solvent interactions in modern physical organic chemistry can be studied using supramolecular polymers. Our aim is to put the recent progress into a historical context and provide avenues toward a more comprehensive understanding of solvents in multicomponent supramolecular systems.

W. Williams, Lingyu Zeng, T. Gensch et al.

Organic chemistry is replete with complex relationships: for example, how a reactant’s structure relates to the resulting product formed; how reaction conditions relate to yield; how a catalyst’s structure relates to enantioselectivity. Questions like these are at the foundation of understanding reactivity and developing novel and improved reactions. An approach to probing these questions that is both longstanding and contemporary is data-driven modeling. Here, we provide a synopsis of the history of data-driven modeling in organic chemistry and the terms used to describe these endeavors. We include a timeline of the steps that led to its current state. The case studies included highlight how, as a community, we have advanced physical organic chemistry tools with the aid of computers and data to augment the intuition of expert chemists and to facilitate the prediction of structure–activity and structure–property relationships.

Yun Zhou, Jianan Wang, Beijing Tian et al.

A concise synthesis of the sex pheromones of elm spanworm as well as painted apple moth has been achieved. The key steps were the alkylation of acetylide ion, Sharpless asymmetric epoxidation and Brown’s P2-Ni reduction. This approach provided the sex pheromone of the elm spanworm (<b>1</b>) in 31% total yield and those of the painted apple moth (<b>2</b>, <b>3</b>) in 26% and 32% total yields. The ee values of three final products were up to 99%. The synthesized pheromones hold promising potential for use in the management and control of these pests.

Roberto Castro-Muñoz

Today, chemistry and nanotechnology cover molecular separations in liquid and gas states by aiding in the design of new nano-sized materials. In this regard, the synthesis and application of two-dimensional (2D) nanomaterials are current fields of research in which structurally defined 2D materials are being used in membrane separation either in self-standing membranes or composites with polymer phases. For instance, pervaporation (PV), as a highly selective technology for liquid separation, benefits from using 2D materials to selectively transport water or other solvent molecules. Therefore, this review paper offers an interesting update in revising the ongoing progress of PV membranes using 2D materials in several applications, including solvent purification (the removal of water from organic systems), organics removal (the removal of organic molecules diluted in water systems), and desalination (selective water transport from seawater). In general, recent reports from the past 3 years have been discussed and analyzed. Attention has been devoted to the proposed strategies and fabrication of membranes for the inclusion of 2D materials into polymer phases. Finally, the future trends and current research gaps are declared for the scientists in the field.

Botao Yu, Frazier N. Baker, Ziru Chen et al.

To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However, their evaluations are narrow in scope, leaving a large gap in understanding the benefits of tools across diverse chemistry tasks. To bridge this gap, we develop ChemToolAgent, an enhanced chemistry agent over ChemCrow, and conduct a comprehensive evaluation of its performance on both specialized chemistry tasks and general chemistry questions. Surprisingly, ChemToolAgent does not consistently outperform its base LLMs without tools. Our error analysis with a chemistry expert suggests that: For specialized chemistry tasks, such as synthesis prediction, we should augment agents with specialized tools; however, for general chemistry questions like those in exams, agents' ability to reason correctly with chemistry knowledge matters more, and tool augmentation does not always help.