ACS Physical Chemistry Au最新文献

Photon upconversion based on triplet–triplet annihilation (TTA-UC) is an attractive wavelength conversion with increasing use in organic synthesis in the homogeneous phase; however, this technology has not performed with canonical solid catalysts yet. Herein, a BOPHY dye covalently anchored on silica is successfully used as a sensitizer in a TTA system that efficiently catalyzes Mizoroki–Heck coupling reactions. This procedure has enabled the implementation of in-flow reaction conditions for the synthesis of a variety of aromatic compounds, and mechanistic proof has been obtained by means of transient absorption spectroscopy.

Layered intercalation compounds, where atoms or molecules (intercalants) are inserted into layered materials (hosts), hold great potential for diverse applications. However, the lack of a systematic understanding of stable host–intercalant combinations poses challenges in materials design due to the vast combinatorial space. In this study, we performed first-principles calculations on 9024 compounds, unveiling a novel linear regression equation based on the principle of hard and soft acids and bases. This equation, incorporating the intercalant ion formation energy and ionic radius, quantitatively reveals the stability factors. Additionally, employing machine learning, we predicted regression coefficients from host properties, offering a comprehensive understanding and a predictive model for estimating the intercalation energy. Our work provides valuable insights into the energetics of layered intercalation compounds, facilitating targeted materials design.

The rise of modern computer science enabled physical chemistry to make enormous progresses in understanding and harnessing natural and artificial phenomena. Nevertheless, despite the advances achieved over past decades, computational resources are still insufficient to thoroughly simulate extended systems from first principles. Indeed, countless biological, catalytic and photophysical processes require ab initio treatments to be properly described, but the breadth of length and time scales involved makes it practically unfeasible. A way to address these issues is to couple theories and algorithms working at different scales by dividing the system into domains treated at different levels of approximation, ranging from quantum mechanics to classical molecular dynamics, even including continuum electrodynamics. This approach is known as multiscale modeling and its use over the past 60 years has led to remarkable results. Considering the rapid advances in theory, algorithm design, and computing power, we believe multiscale modeling will massively grow into a dominant research methodology in the forthcoming years. Hereby we describe the main approaches developed within its realm, highlighting their achievements and current drawbacks, eventually proposing a plausible direction for future developments considering also the emergence of new computational techniques such as machine learning and quantum computing. We then discuss how advanced multiscale modeling methods could be exploited to address critical scientific challenges, focusing on the simulation of complex light-harvesting processes, such as natural photosynthesis. While doing so, we suggest a cutting-edge computational paradigm consisting in performing simultaneous multiscale calculations on a system allowing the various domains, treated with appropriate accuracy, to move and extend while they properly interact with each other. Although this vision is very ambitious, we believe the quick development of computer science will lead to both massive improvements and widespread use of these techniques, resulting in enormous progresses in physical chemistry and, eventually, in our society.

The enthalpies of formation are computed for a large number of per- and poly fluoroalkyl substances (PFAS) using a connectivity-based hierarchy (CBH) approach. A combination of different electronic structure methods are used to provide the reference data in a hierarchical manner. The ANL0 method, in conjunction with the active thermochemical tables, provides enthalpies of formation for smaller species with subchemical accuracy. Coupled-cluster theory with explicit correlations are used to compute enthalpies of formation for intermediate species, based upon the ANL0 results. For the largest PFAS, including perfluorooctanoic acid (PFOA) and heptafluoropropylene oxide dimer acid (GenX), coupled-cluster theory with local correlations is used. The sequence of homodesmotic reactions proposed by the CBH are determined automatically by a new open-source code, AutoCBH. The results are the first reported enthalpies of formation for the majority of the species. A convergence analysis and global uncertainty quantification confirm that the enthalpies of formation at 0 K should be accurate to within ±5 kJ/mol. This new approach is not limited to PFAS, but can be applied to many chemical systems.

Miniaturization has been the driving force in contemporary technologies. However, two main obstacles limit further progress: additional reduction in size has reached its quantum limit, and lithography has reached its threshold. Future progress requires tackling three challenges: chemical synthesis of a complete device, active cooling for exploiting quantum characteristics, and quantum coherent control for operation. Chemical synthesis replaces the current top-bottom approach to manufacturing with bottom-up synthesis from elementary building blocks. New ultracold synthetic methods should be developed. An additional challenge is the active cooling of molecules, where the bottleneck is entropy removal. Notably, the current solution, namely, diffusion, is too slow. A coherent approach offers a possible solution; specifically, quantum coherent control is the method of choice for manipulating ultracold matter. Finally, the many degrees of freedom of molecules should be an asset that allows the design and implementation of complex tasks such as sensing communication and computing.

Phase-separated protein accumulation through the formation of several aggregate species is linked to the pathology of several human disorders and diseases. Our current investigation envisaged detailed Raman signature and structural intricacy of bovine insulin in its various forms of aggregates produced in situ at an elevated temperature (60 °C). The amide I band in the Raman spectrum of the protein in its native-like conformation appeared at 1655 cm^–1 and indicated the presence of a high content of α-helical structure as prepared freshly in acidic pH. The disorder content (turn and coils) also was predominately present in both the monomeric and oligomeric states and was confirmed by the presence shoulder amide I maker band at ∼1680 cm^–1. However, the band shifted to ∼1671 cm^–1 upon the transformation of the protein solution into fibrillar aggregates as produced for a longer time of incubation. The protein, however, maintained most of its helical conformation in the oligomeric phase; the low-frequency backbone α-helical conformation signal at ∼935 cm^–1 was similar to that of freshly prepared aqueous protein solution enriched in helical conformation. The peak intensity was significantly weak in the fibrillar aggregates, and it appeared as a good Raman signature to follow the phase separation and the aggregation behavior of insulin and similar other proteins. Tyrosine phenoxy moieties in the protein may maintained its H-bond donor–acceptor integrity throughout the course of fibril formation; however, it entered in more hydrophobic environment in its journey of fibril formation. In addition, it was noticed that oligomeric bovine insulin maintained the orientation/conformation of the disulfide bonds. However, in the fibrillar state, the disulfide linkages became more strained and preferred to maintain a single conformation state.

Thermodynamic hypotheses and models are the touchstone for chemical results, but the actual models based on time-invariance, which have performed efficiently in the development of chemistry, are nowadays invalid for the interpretation of the behavior of complex systems exhibiting nonlinear kinetics and with matter and energy exchange flows with the surroundings. Such fields of research will necessarily foment and drive the use of thermodynamic models based on the description of irreversibility at the macroscopic level, instead of the current models which are strongly anchored in microreversibility.

The ability to relate substituent electronic effects to chemical reactivity is a cornerstone of physical organic chemistry and Linear Free Energy Relationships. The computation of electronic parameters is increasingly attractive since they can be obtained rapidly for structures and substituents without available experimental data and can be applied beyond aromatic substituents, for example, in studies of transition metal complexes and aliphatic and radical systems. Nevertheless, the description of “top-down” macroscopic observables, such as Hammett parameters using a “bottom-up” computational approach, poses several challenges for the practitioner. We have examined and benchmarked the performance of various computational charge schemes encompassing quantum mechanical methods that partition charge density, methods that fit charge to physical observables, and methods enhanced by semiempirical adjustments alongside NMR values. We study the locations of the atoms used to obtain these descriptors and their correlation with empirical Hammett parameters and rate differences resulting from electronic effects. These seemingly small choices have a much more significant impact than previously imagined, which outweighs the level of theory or basis set used. We observe a wide range of performance across the different computational protocols and observe stark and surprising differences in the ability of computational parameters to capture para- vs meta-electronic effects. In general, σ_m predictions fare much worse than σ_p. As a result, the choice of where to compute these descriptors─for the ring carbons or the attached H or other substituent atoms─affects their ability to capture experimental electronic differences. Density-based schemes, such as Hirshfeld charges, are more stable toward unphysical charge perturbations that result from nearby functional groups and outperform all other computational descriptors, including several commonly used basis set based schemes such as Natural Population Analysis. Using attached atoms also improves the statistical correlations. We obtained general linear relationships for the global prediction of experimental Hammett parameters from computed descriptors for use in statistical modeling studies.

This manuscript outlines my outlook on the development of electric-field (EF)-mediated-chemistry and the vision of its state by 2050. I discuss applications of oriented-external electric-fields (OEEFs) on chemical reactions and proceed with relevant experimental verifications. Subsequently, the Perspective outlines other ways of generating EFs, e.g., by use of pH-switchable charges, ionic additives, water droplets, and so on. A special section summarizes conceptual principles for understanding and predicting OEEF effects, e.g., the “reaction-axis rule”, the capability of OEEFs to act as tweezers that orient reactants and accelerate their reaction, etc. Finally, I discuss applications of OEEFs in continuous-flow setups, which may, in principle, scale-up to molar concentrations. The Perspective ends with the vision that by 2050, OEEF usage will change chemical education, if not also the art of making new molecules.