Precision Chemistry最新文献

The reaction dynamics for the H + HeH⁺ → He + H₂ ⁺ reaction in its electronic ground state is investigated using two different representations of the potential energy surface (PES). The first uses a combined kernel and neural network representation of UCCSD-(T) reference data whereas the second is a corrected PES (cR-PES) that eliminates an artificial barrier in the entrance channel appearing in its initial expansion based on full configuration interaction reference data. Despite the differences between the two PESs, both yield k _v=0,j=0 ≈ 2 × 10^-9 cm³/molecule/s at T = 10 K which is consistent with a T-independent Langevin rate k _L = 2.1 × 10^-9 cm³/molecule/s but considerably larger than the only experimentally reported value k _ICR = (9.1 ± 2.5) × 10^-10 cm³/molecule/s from ion cyclotron resonance experiments. Similarly, branching ratios for the reaction outcomes are comparable for the two PESs. However, when analyzing less averaged properties such as initial state-selected T-dependent rate coefficients and final vibrational states of the H₂ ⁺ product for low temperatures, the differences in the two PESs manifest themselves in the observables. Thus, depending on the property analyzed, accurate and globally valid representations of the PES are required, whereas more approximate and empirical construction schemes can be followed for state-averaged observables.

Two-dimensional (2D) lateral heterostructures, an interesting class of nanostructures, have shown great promise in optoelectronics and nanoelectronics due to their unique electronic and optical properties. In recent years, significant progress has been made in the controlled growth of 2D lateral heterostructures. However, challenges remain in areas such as material selection and compatibility, interface quality, and precise control over the growth process. High-quality interfaces are critical for the optoelectronic performance of these heterostructures, yet ensuring uniformity and consistency during fabrication continues to be a major obstacle. This review provides a comprehensive overview of the recent developments in the controlled growth of 2D lateral heterostructures. It examines the fabrication methods for various types of 2D lateral heterostructures and their associated challenges. The review also discusses the properties and potential applications of these heterostructures, aiming to offer a deeper understanding of their preparation, characteristics, and future prospects. By identifying existing challenges and opportunities in the fabrication process, this work seeks to guide future advancements in the field and support the efficient large-scale production of high-quality 2D lateral heterostructures.

The unavailability of accurate and reliable methods for early ovarian cancer detection represents a major gap in ovarian cancer diagnosis and management. The emergence and recent integration of machine learning with cancer diagnostic techniques, particularly biomarker-based blood tests, have the potential to improve the selectivity and sensitivity of ovarian cancer detection substantially. Herein, we leverage a series of machine learning and statistical approaches to analyze clinically relevant data sets of more than 300 patients with ovarian tumors and 47 blood-obtained features to distinguish between cancerous and benign tumors. We found that HE4, CA125, menopausal status, and age were some of the most important features distinguishing cancerous from benign ovarian tumors in all patient populations. Age was noted to be a critical feature with cancer discriminatory power only in premenopausal patients but less so in postmenopausal patients. Systematic consideration of patient menopausal status, types of machine learning algorithms, and number of clinical features is necessary prior to ovarian cancer screening to yield more accurate and reliable diagnostic results. Overall, this study provides deeper insight into the use of machine learning, feature selection, and other relevant quantitative approaches to advance ovarian cancer diagnosis to improve patient outcomes.

Dynamic molecular systems capable of controlled transformations are foundational for developing next-generation intelligent materials and sensors. However, achieving sequential, multistate switching with distinct optical outputs on a single molecular platform remains challenging. Here, we introduce a class of dynamic fluorescent systems built upon a single benzo-conjugated acceptor. This system undergoes programmed molecular reconfiguration and fluorescence switching through sequential chemical and pH-driven triggers, leveraging intramolecular oxa/thiol-Michael addition-elimination reactions via dynamic covalent bonding in aqueous medias. Each distinct molecular state exhibits unique, trackable absorbance and fluorescence signatures, governed by precisely controlled pseudo-pK _a values. We demonstrate the utility of this system by achieving real-time, noninvasive optical tracking of topological transitions in soft materials, specifically monitoring hydrogel degradation and reformation (gel-sol-gel). Furthermore, by tuning the molecular scaffold, we developed derivatives for live-cell imaging, enabling dynamic visualization of intracellular pH fluctuations. This work presents a versatile platform for designing programmable, multistimuli-responsive molecular systems with potential in adaptive materials, chemical sensing, and advanced biomedical diagnostics.

Atomically precise metal nanocluster (MNC) catalysts represent a significant advancement in electrocatalysis, particularly for the electrocatalytic nitrate reduction reaction (eNO₃RR). Their distinct structural features, which include precisely defined geometric and electronic configurations, enhance catalytic performance. Additionally, low nuclearity MNCs possess unique metallic properties that exhibit various active sites, optimizing the adsorption and conversion of nitrate intermediates. This functionality is vital for improving both reaction kinetics and selectivity during eNO₃RR. Recent investigations have shown that by precisely adjusting the size, ligand, and composition of these nanoclusters, researchers can achieve specific electrochemical properties beneficial for eNO₃RR. Capitalizing on their atomically precise nature can significantly enhance the efficiency and sustainability of eNO₃RR processes. MNCs also offer the flexibility to explore diverse ligands, supporting materials, and integration with other catalytic frameworks to further enhance eNO₃RR activity. In this Perspective, we aim to consolidate recent advancements in the development and application of atomically precise MNCs in eNO₃RR, emphasizing their potential to transform electrocatalytic processes and contribute to cleaner nitrogen cycle. We hope that this Perspective will motivate more researchers to delve into the various dimensions of MNCs to deepen their understanding of the structure-activity correlations in eNO₃RR and beyond.