Gakushuin University

gem-Difluoroalkenes are valuable carbonyl bioisosteres in medicinal chemistry. Conventional synthesis primarily involves nucleophilic addition to α-trifluoromethyl styrenes. In contrast, recent approaches leverage photocatalytic radical addition, followed by reduction and fluoride elimination. Reaction pathways utilizing gem-difluoroallyl radicals remain largely underdeveloped despite their potential to enhance synthetic diversity. We report herein an electron donor-acceptor (EDA) complex-mediated C-F bond photoreduction of αtrifluoromethyl styrenes for the synthesis of gem-difluoroalkenes via gem-difluoroallyl radical addition. Radical trapping experiments confirm the formation of gem-difluoroallyl radical intermediates. By integrating this radical pathway with a conventional nucleophilic addition strategy, regiodivergent synthesis of gem-difluoroalkenes is achieved. To the best of our knowledge, this work is the first example of C–F bond photoreduction and functionalization of α-trifluoromethyl styrenes via gem-difluoroallyl radicals, forming gem-difluoroalkenes.

Even when the crystal structure and alloy composition are fixed, the arrangement of different atoms on crystal sites can significantly affect material properties and formation processes. To address this, we have been developing a research framework called Material Configurations Informatics (MCI), which focuses on the degrees of freedom in atomic configurations through computational science and machine learning approaches. With increasing computational power, large-scale first-principles calculations (DFT) have become feasible. However, the biggest challenge in MCI is the enormous number of possible atomic configurations. We tackle this problem using techniques such as efficient sampling via Bayesian optimization and interaction modeling with approaches like the Ising model. In this presentation, we introduce applications of MCI to issues such as the mixing of different surface reconstructions—an important factor in discussions of crystal growth processes—and the study of semiconductor properties in 2D alloy materials.

The Orange carotenoid protein (OCP) is a blue-green light sensor that regulates nonphotochemical quenching in cyanobacteria through reversible transitions between its dark-adapted (OCPO) and light-adapted (OCPR) states. Despite extensive studies, the detailed reaction scheme remains unclear. In this study, we examined the photoinduced reaction dynamics of OCP using size-exclusion chromatography (SEC), small-angle X-ray scattering (SAXS), and transient grating (TG) spectroscopy. We found that OCPO and OCPR exist in monomer-dimer equilibria, but the OCPR dimer forms a more stable and elongated shape than the OCPO dimer. The TG measurements revealed that upon photoexcitation, OCPO monomers undergo two structural transitions before associating into OCPR dimers. In contrast, the OCPO dimer dissociates prior to the structural rearrangement, highlighting a fundamental difference in their reaction pathways. Moreover, the dimerization was found to moderately reduce the photoreactivity of OCPO compared to the monomer. We also found that apo-OCP readily forms a heterodimer with OCPR, potentially altering reaction pathways and changing intrinsic kinetic behavior.

Detecting and addressing discrimination is one of the most crucial ways for ethnic and sexual minorities and women to fully integrate into the society. However, humans often fail to correctly judge what constitutes discrimination. The recent rapid development of artificial intelligence (AI) based on large language models (LLMs) has shown promise in assisting with this task, although its performance is understudied. This study investigates how humans and LLM-based AI, such as OpenAI’s ChatGPT, detect the concept of ‘discrimination’ and how their representation differs. Specifically, surveys were conducted asking humans (Japanese respondents) and ChatGPT (GPT-4) to evaluate the degree of discrimination in hypothetical unequal treatment scenarios presented to them. The scenarios varied in terms of targets, including their ethnicity, gender, and sexuality, and types of discrimination, such as those based on tastes, stereotypes, and statistics. The results show that ChatGPT generally classifies the scenarios as more discriminatory than humans do. However, ChatGPT also shares a tendency with humans to be more tolerant of unequal treatment based on ethnicity and gender compared to sexuality, and it is less likely to detect statistical discrimination than taste- or stereotype-based discrimination. Although LLM-based AI presents a potential tool for addressing discrimination and can offer temporary solutions, it may not fully capture all types of discrimination.

The nonmonotonic temperature dependence and sign reversal of chirality-related anomalous Hall effect in highly conductive metals are studied. Through the analysis of scattering rate, we find that the nonmonotonicity and sign reversal have two major origins: (1)competition between the contribution from short-range and long-range spin correlations and (2)nonmonotonic spin correlation in the high field. The former mechanism gives rise to nonmonotonic temperature dependence in a wide range of electron density and, in some cases, a sign reversal of Hall resistivity as the temperature decreases. On the other hand, the latter mechanism is responsible for the sign reversal of Hall conductivity in the high field, which sign reversal generally occurs in magnets with antiferromagnetic interactions. The results demonstrate how the Hall effect reflects local spin correlation and provide insights into the mechanism of nonmonotonicity and sign reversal of the anomalous Hall effect by spin chirality.

Biochemical analysis of living systems such as singlecells benefitsgreatly from the label-free and low-invasive molecular mapping withRaman microspectroscopy. Sets of Raman spectra at different spatialpoints are analyzed to generate Raman molecular maps correspondingto specific chemical species. However, human error and subjectivedata analysis can be technical issues that limit interpretation andits validity. Here, we present an objective data analysis scheme forpostprocessing large data sets of Raman spectra for molecular mappingof living cells. The process comprises three steps: (i) Denoisingthe spectral data set using low-rank approximation; (ii) obtainingan objective background from data points outside the target cell;(iii) subtracting the thus obtained background using HypotheticalAddition Multivariate Analysis with Numerical Differentiation (HAMAND)via an automatically determined coefficient. Through the present analysis,minor Raman peaks, as indiscernible as they are, can be identifiedand precisely mapped. We demonstrate a quantitative discussion ofcellular components after extracting contributions only from a singletarget cell from a Raman mapping image where multiple cells or partsof other cells are present. This work opens an improved analysis workflowfor accurate spectroscopic analysis of living cells with the advantageof identifying minor Raman peaks unambiguously.