The Weiner's theory (WT) is developed on the basis of the exactly solvable Schrödinger equation with trigonometric double-well potential (TDWP). The symmetric case of TDWP is considered. This modified version of WT (mWT) enables one to eliminate some severe approximations of the original Weiner's approach and to obtain more accurate results. An analytic formula is derived which provides the calculation of the proton transfer rate with the help of elements implemented in {\sl {Mathematica}}. We exemplify the application of mWT by calculating the proton transfer rate constant in the hydrogen bond of the proton-bound ammonia dimer cation ${\rm{N_2H_7^{+}}}$ (${\rm{H_3N\cdot\cdot\cdot H^{+} \cdot\cdot\cdot NH_3}}$). The parameters of the model for this object are extracted from available literature data on IR spectroscopy and quantum chemical calculations. The approach yields the transition from the Arrhenius-like exponential temperature dependence characteristic of thermal activation to that of quantum tunneling. Besides it is well suited for describing the phenomenon of vibrationally enhanced tunnelling.
Koushlesh Ranjan, Basanti Barar, Minakshi Prasad
et al.
Abstract Electrochemical sensors, detecting biochemical changes through electrical signals, play a pivotal role in point-of-care diagnostics, especially for detecting specific biomarkers for diseases including cancer, diabetes, obesity, cardiovascular conditions, etc. Biosensor based technology faces numerous challenges including signal complexity, noise, data interpretation, etc. These challenges influence the sensitivity and selectivity of the techniques and limit their wider applications. The modern-day miracle, Artificial Intelligence (AI) offers transformative solutions to these challenges. The applications of machine learning (ML) algorithms and AI in electrochemical data analysis have significantly enhanced the sensitivity and specificity of diagnostic methods. The AI-powered systems can easily identify the specific patterns within the electrochemical signals that otherwise remain undetectable by traditional methods. This leads to early detection, personalized treatment plans, and real-time monitoring of diseases. AI also assists in optimizing sensor design, manages large datasets, and improves the performance and reliability of electrochemical diagnostics (ED) devices. Thus, the integration of AI into ED is transforming the healthcare sector by providing faster, more precise, and cost-effective diagnostic solutions.
Chemical technology, Physical and theoretical chemistry
Baroque questions of set-theoretic foundations are widely assumed to be irrelevant to physics. In this article, I demonstrate that this assumption is incorrect. I show that the fundamental physical question of whether a theory is deterministic, whether it fixes a unique future given the present, can depend on choice of set-theoretic axiom candidates over which there is intractable disagreement. This dependence is not confined to hypothetical examples. It reaches into mainstream, discrete, and frontier physics, including the dynamics of Kerr black hole interiors. One upshot is that either physical theories must be relativized to set-theoretic metatheories (in which case physics itself becomes relative), or the search for new axioms to settle undecidables may admit of empirical input.
Starting from Greg Moore's description about Physical Mathematics, a framework is proposed in order to understand it, based on Gilles Châtelet's philosophy. It will be argued that Châtelet's ideas of inverting, splitting, augmenting and virtuality are crucial in the discussion about the nature of Physical Mathematics. Along this line, it will be proposed that mirror symmetry is a natural study case to test Châtelet's ideas in this context. This should be considered as a first step in a long term project aiming to study the relations among mathematics, physics and philosophy in the construction of a global understanding of the structure of the universe, as it was envisioned by Grothendieck in the late 80's of the last century and it was started to be developed independently by Châtelet in the beginning of the 90's. The main suggestion of the essay is that it is in the relations between mathematics, physics and philosophy that new knowledge arises.
Jan-Lucas Eickmann, Kai-Hong Luo, Mikhail Roiz
et al.
Simulating vibronic spectra is a central task in physical chemistry, offering insight into important properties of molecules. Recently, it has been experimentally demonstrated that photonic platforms based on Gaussian boson sampling (GBS) are capable of performing these simulations. However, whether an actual GBS approach is required depends on the molecule under investigation. To develop a better understanding on the requirements for simulating vibronic spectra, we explore connections between theoretical approximations in physical chemistry and their photonic counterparts. Mapping these approximations into photonics, we show that for certain molecules the GBS approach is unnecessary. We place special emphasis on the linear coupling approximation, which in photonics corresponds to sampling from multiple coherent states. By implementing this approach in experiments, we demonstrate improved similarities over previously reported GBS results for formic acid and identify the particular attributes that a molecule must exhibit for this, and other approximations, to be valid. These results highlight the importance in forming deeper connections between traditional methods and photonic approaches.
Multimodal Large Language Models (MLLMs) have seen growing adoption across various scientific disciplines. These advancements encourage the investigation of molecule-text modeling within synthetic chemistry, a field dedicated to designing and conducting chemical reactions to synthesize new compounds with desired properties and applications. Current approaches, however, often neglect the critical role of multiple molecule graph interaction in understanding chemical reactions, leading to suboptimal performance in synthetic chemistry tasks. This study introduces PRESTO(Progressive Pretraining Enhances Synthetic Chemistry Outcomes), a new framework that bridges the molecule-text modality gap by integrating a comprehensive benchmark of pretraining strategies and dataset configurations. It progressively improves multimodal LLMs through cross-modal alignment and multi-graph understanding. Our extensive experiments demonstrate that PRESTO offers competitive results in downstream synthetic chemistry tasks. The code can be found at https://github.com/IDEA-XL/PRESTO.
Joseph P. Heindel, R. Allen LaCour, Teresa Head-Gordon
In charged water microdroplets, which occur in nature or in the lab upon ultrasonication or in electrospray processes, the thermodynamics for reactive chemistry can be dramatically altered relative to the bulk phase. Here, we provide a theoretical basis for the observation of accelerated chemistry by simulating water droplets of increasing charge imbalance to create redox agents such as hydroxyl and hydrogen radicals and solvated electrons. We compute the hydration enthalpy of OH^- and H^+ that controls the electron transfer process, and the corresponding changes in vertical ionization energy and vertical electron affinity of the ions, to create OH* and H* reactive species. We find that at ~20-50% of the Rayleigh limit of droplet charge the hydration enthalpy of both OH^- and H^+ have decreased by >50 kcal/mol such that electron transfer becomes thermodynamically favorable, in correspondence with the more favorable vertical electron affinity of H^+ and the lowered vertical ionization energy of OH^-. We provide scaling arguments that show that the nanoscale calculations and conclusions extend to the experimental microdroplet length scale. The relevance of the droplet charge for chemical reactivity is illustrated for the formation of H2O2, and has clear implications for other redox reactions observed to occur with enhanced rates in microdroplets.
Diwash Dhakal, Darren M. Driscoll, N. Govind
et al.
The emergence of cation-anion species, or contact ion pairs, is fundamental to understanding the physical properties of aqueous solutions when moving from the ideal, low-concentration limit to the manifestly non-ideal limits of very high solute concentration or constituent ion activity. We focus here on Zn halide solutions both as a model system and also as an exemplar of the applications spanning from (i) electrical energy storage via the paradigm of water in salt electrolyte (WiSE) to (ii) the physical chemistry of brines in geochemistry to (iii) the long-standing problem of nucleation. Using a combination of experimental and theoretical approaches we quantify the halide coordination number and changing coordination geometry without embedded use of theoretical equilibrium constants. These results and the associated methods, notably including the use of valence-to-core X-ray emission spectroscopy, provide new insights into the Zn halide system and new research directions in the physical chemistry of concentrated electrolytes.
Md. Atikur Rahmana, Mahbub Hasana, Rukaia Khatuna
et al.
DFT simulation based ab-initio approach has been executed for investigating the comparative study of the physical properties of superconducting disilicide materials YT$_2$Si$_2$ (T= Co, Ni, Ru, Rh, Pd, Ir). This is the first comparative theoretical investigation of these materials, which is done through Cambridge Serial Total Energy Package module.
Grigorii V. Smirnov-Pinchukov, Tamara Molyarova, Dmitry A. Semenov
et al.
Aims. With the large amount of molecular emission data from (sub)millimeter observatories and incoming James Webb Space Telescope infrared spectroscopy, access to fast forward models of the chemical composition of protoplanetary disks is of paramount importance. Methods. We used a thermo-chemical modeling code to generate a diverse population of protoplanetary disk models. We trained a K-nearest neighbors (KNN) regressor to instantly predict the chemistry of other disk models. Results. We show that it is possible to accurately reproduce chemistry using just a small subset of physical conditions, thanks to correlations between the local physical conditions in adopted protoplanetary disk models. We discuss the uncertainties and limitations of this method. Conclusions. The proposed method can be used for Bayesian fitting of the line emission data to retrieve disk properties from observations. We present a pipeline for reproducing the same approach on other disk chemical model sets.
The idea to use quantum mechanical devices to simulate other quantum systems is commonly ascribed to Feynman. Since the original suggestion, concrete proposals have appeared for simulating molecular and materials chemistry through quantum computation, as a potential ``killer application''. Indications of potential exponential quantum advantage in artificial tasks have increased interest in this application, thus, it is critical to understand the basis for potential exponential quantum advantage in quantum chemistry. Here we gather the evidence for this case in the most common task in quantum chemistry, namely, ground-state energy estimation. We conclude that evidence for such an exponential advantage across chemical space has yet to be found. While quantum computers may still prove useful for quantum chemistry, it may be prudent to assume exponential speedups are not generically available for this problem.
The parsley extract (PLE) was prepared using absolute ethyl alcohol. The PLE and synergistic iodide were firstly utilized as efficacious corrosion inhibitors to slow down the corrosion rate of carbon steel-Q235 in 0.5 mol/L H2SO4 solution. The anti-corrosion performance was researched by weight loss method, electrochemical tests, surface analysis and quantum chemistry calculation. Results of electrochemical and weight loss tests show that the synergetic PLE and I− exhibit the optimal corrosion inhibition efficiency 99%. The combined inhibitor displays the favorable long-term corrosion inhibition effect, and the inhibition efficiency can maintain more than 90% after 144 h immersion. The introduction of I− makes carbon steel surface with higher negative charge amount, which could be beneficial to the interaction between corrosion inhibitor and Fe atoms. The adsorption behavior obeys the Langmuir isotherm adsorption, and involves chemical and physical adsorption. On the basis of electrochemical consequences and theoretical calculation, the adsorption process and anti-corrosion mechanisms are further explored.
MXenes and MBenes emerge from a large family of two-dimensional (2D) transition metal carbides, nitrides, carbonitrides, and borides and have drawn tremendous attention in recent years. They possess diverse elemental compositions, surface terminations and geometrical structures, and exhibit many fascinating physical and chemical properties. Specifically, these 2D compounds hold great promise for renewable energy applications owing to their superior electrical conductivity, high hydrophility, rich surface chemistry, and outstanding stability. In this perspective, we present a brief overview about the catalytic properties of MXenes, MBenes and MXene based heterostructures for typical electrochemical reactions. The roles of surface, edge and interface of these 2D nanostructures in energy conversion are highlighted from a theoretical point of view. Challenges and outlooks for future research are also outlined.
Takumi KUSANO, Keita FUNAYAMA, Yukihiro TADOKORO
et al.
Potential barrier plays a key role in ionic transport through nanoporous membranes. We numerically study the contribution of the potential barrier to the ionic current through a cylindrical Si3N4 nanopore using molecular dynamics simulations. We extract the height of the potential barrier from the best fit using a simple polynomial model of the ionic current. We reveal that the surface atoms make a contribution to the potential barrier. This study provides the height of the potential barrier so that it is valuable information about the underlying mechanism of ionic transport through nanopores.
There has been a surge in the interest of using machine learning techniques to assist in the scientific process of formulating knowledge to explain observational data. We demonstrate the use of Bayesian Hidden Physics Models to first uncover the physics governing the propagation of acoustic impulses in metallic specimens using data obtained from a pristine sample. We then use the learned physics to characterize the microstructure of a separate specimen with a surface-breaking crack flaw. Remarkably, we find that the physics learned from the first specimen allows us to understand the backscattering observed in the latter sample, a qualitative feature that is wholly absent from the specimen from which the physics were inferred. The backscattering is explained through inhomogeneities of a latent spatial field that can be recognized as the speed of sound in the media.