Cavity Manipulation of Attosecond Charge Migration in Conjugated Dendrimers.

Dendrimers are branched polymers with wide applications to photosensitization, photocatalysis, photodynamic therapy, photovoltaic conversion, and light sensor amplification. The primary step of numerous photophysical and photochemical processes in many molecules involves ultrafast coherent electronic dynamics and charge oscillations triggered by photoexcitation. This electronic wavepacket motion at short times where the nuclei are frozen is known as attosecond charge migration.

Rapid room-temperature phosphorescence chiral recognition of natural amino acids.

Chiral recognition of amino acids is very important in both chemical and life sciences. Although chiral recognition with luminescence has many advantages such as being inexpensive, it is usually slow and lacks generality as the recognition module relies on structural complementarity. Here, we show that one single molecular-solid sensor, L-phenylalanine derived benzamide, can manifest the structural difference between the natural, left-handed amino acid and its right-handed counterpart via the difference of room-temperature phosphorescence (RTP) irrespective of the specific chemical structure.

Water-Ice Microstructures and Hydration States of Acridinium Iodide Studied by Phosphorescence Spectroscopy.

Ice has been suggested to have played a significant role in the origin of life partly owing to its ability to concentrate organic molecules and promote reaction efficiency. However, the techniques for studying organic molecules in ice are absorption-based, which limits the sensitivity of measurements. Here we introduce an emission-based method to study organic molecules in water ice: the phosphorescence displays high sensitivity depending on the hydration state of an organic salt probe, acridinium iodide (ADI).

A robotic AI-Chemist system for multi-modal AI-ready database.

By fusing literature data mining, high-performance simulations, and high-accuracy experiments, robotic AI-Chemist can achieve automated high-throughput production, classification, cleaning, association and fusion of data, and thus develop a multi-modal AI-ready database.

Spectroscopy-Guided Deep Learning Predicts Solid-Liquid Surface Adsorbate Properties in Unseen Solvents.

Accurately and rapidly acquiring the microscopic properties of a material is crucial for catalysis and electrochemistry. Characterization tools, such as spectroscopy, can be a valuable tool to infer these properties, and when combined with machine learning tools, they can theoretically achieve fast and accurate prediction results. However, on the path to practical applications, training a reliable machine learning model is faced with the challenge of uneven data distribution in a vast array of non-negligible solvent types.

Label-Free Data Mining of Scientific Literature by Unsupervised Syntactic Distance Analysis.

Label-free data mining can efficiently feed large amounts of data from the vast scientific literature into artificial intelligence (AI) processing systems. Here, we demonstrate an unsupervised syntactic distance analysis (SDA) approach that is capable of mining chemical substances, functions, properties, and operations without annotation. This SDA approach was evaluated in several areas of research from the physical sciences and achieved performance in information mining comparable to that of supervised learning, as shown by its satisfactory scores of 0.

A Quick Method for Predicting Reflectance Spectra of Nanophotonic Devices via Artificial Neural Network.

Nanophotonics use the interaction between light and subwavelength structures to design nanophotonic devices and to show unique optical, electromagnetic, and acoustic properties that natural materials do not have. However, this usually requires considerable expertise and a lot of time-consuming electromagnetic simulations. With the continuous development of artificial intelligence, people are turning to deep learning for designing nanophotonic devices.

Disorder-Enhanced Charge-Transfer-Mediated Room-Temperature Phosphorescence in Polymer Media.

Room-temperature phosphorescence (RTP) polymers have important applications for biological imaging, oxygen sensing, data encryption, and photodynamic therapy. Despite the many advantages polymeric materials offer such as great control over gas permeability and processing flexibility, disorder is traditionally considered as an intrinsic negative impact on the efficiency for embedded RTP luminophores, as various allowed thermal motions could quench the emitting states. However, we propose that such disorder-enabled freedoms of microscopic motions can be beneficial for charge-transfer-mediated RTP, which is facilitated by molecular conformational changes among different electronic transition states.

Application of an Enhanced Whale Optimization Algorithm on Coverage Optimization of Sensor.

The wireless sensor network (WSN) is an essential technology of the Internet of Things (IoT) but has the problem of low coverage due to the uneven distribution of sensor nodes. This paper proposes a novel enhanced whale optimization algorithm (WOA), incorporating Lévy flight and a genetic algorithm optimization mechanism (WOA-LFGA). The Lévy flight technique bolsters the global search ability and convergence speed of the WOA, while the genetic optimization mechanism enhances its local search and random search capabilities.

Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers.

The rapid progress of machine learning (ML) in predicting molecular properties enables high-precision predictions being routinely achieved. However, many ML models, such as conventional molecular graph, cannot differentiate stereoisomers of certain types, particularly conformational and chiral ones that share the same bonding connectivity but differ in spatial arrangement. Here, we designed a hybrid molecular graph network, Chemical Feature Fusion Network (CFFN), to address the issue by integrating planar and stereo information of molecules in an interweaved fashion.

An all-round AI-Chemist with a scientific mind.

The realization of automated chemical experiments by robots unveiled the prelude to an artificial intelligence (AI) laboratory. Several AI-based systems or robots with specific chemical skills have been demonstrated, but conducting all-round scientific research remains challenging. Here, we present an all-round AI-Chemist equipped with scientific data intelligence that is capable of performing basic tasks generally required in chemical research.

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features.

Research Progress of p-Type Oxide Thin-Film Transistors.

The development of transparent electronics has advanced metal-oxide-semiconductor Thin-Film transistor (TFT) technology. In the field of flat-panel displays, as basic units, TFTs play an important role in achieving high speed, brightness, and screen contrast ratio to display information by controlling liquid crystal pixel dots. Oxide TFTs have gradually replaced silicon-based TFTs owing to their field-effect mobility, stability, and responsiveness.

Origin of Red-Shifted Phosphorescence from Triphenylamines: Triplet Excimer or Impurity?

Triphenylamine (TPA) was reported to exhibit temperature-dependent dual phosphorescence, where the red-shifted band was assigned as the excimeric phosphorescence with an energy shift of >3000 cm (J. Phys. Chem.

Microstructure and mechanical properties of a newly developed low Young's modulus Ti-15Zr-5Cr-2Al biomedical alloy.

The Ti-15Zr-5Cr-2Al alloy has been developed and various heat treatments have been investigated to develop new biomedical materials. It is found that the heat treatment conditions strongly affect the phase constitutions and mechanical properties. The as-cast specimen is comprised of β phase and a small fraction of α phase, which is attributed to the suppression of ω phase caused by adding Al.

External Heavy-Atom Effect via Orbital Interactions Revealed by Single-Crystal X-ray Diffraction.

Enhanced spin-orbit coupling through external heavy-atom effect (EHE) has been routinely used to induce room-temperature phosphorescence (RTP) for purely organic molecular materials. Therefore, understanding the nature of EHE, i.e.