Chemistry Research Articles

Development and Characterization of Advanced Nano-Coating with CeO₂, Al₂O₃, ZnO, and TiO₂ Nanoparticles for Enhanced Durability and Corrosion Resistance

This study introduces an innovative sol-gel nano-coating designed to address silica scaling and corrosion in hydrothermal and high-stimulation environments. By incorporating CeO₂, Al₂O₃, ZnO, and TiO₂ nanoparticles into a polymer matrix through precise sonication techniques, the coating achieves uniform dispersion and enhanced functional performance. The meticulous characterization of nanoparticle integration optimizes mechanical strength, thermal stability, and chemical resistance. Applied via a directional supplying device, the nano-coating is mechanically deposited onto the cemented walls of wells, providing robust protection against scaling and corrosion across diverse conditions, including varying temperatures and pH levels. Laboratory testing demonstrates significant reductions in silica scaling and corrosion rates, confirming the coating's efficacy in maintaining production efficiency in both hydrothermal projects and fracking operations. This paper details the synthesis process and molecular chemistry involved in nanoparticle integration, alongside the resultant structural properties of the coating. Thermogravimetric analysis (TGA) assesses thermal stability, while kinetic models such as Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) evaluate activation energy without assuming specific reaction models. The findings highlight the potential of this advanced nano-coating as a robust solution for enhancing operational longevity and efficiency in challenging industrial applications.

Weighted Asymmetry Index: A New Graph-Theoretic Measure for Network Analysis and Optimization

Graph theory is a crucial branch of mathematics in fields like network analysis, molecular chemistry, and computer science, where it models complex relationships and structures. Many indices are used to capture the specific nuances in these structures. In this paper, we propose a new index, the weighted asymmetry index, a graph-theoretic metric quantifying the asymmetry in a network using the distances of the vertices connected by an edge. This index measures how uneven the distances from each vertex to the rest of the graph are when considering the contribution of each edge. We show how the index can capture the intrinsic asymmetries in diverse networks and is an important tool for applications in network analysis, optimization problems, social networks, chemical graph theory, and modeling complex systems. We first identify its extreme values and describe the corresponding extremal trees. We also give explicit formulas for the weighted asymmetry index for path, star, complete bipartite, complete tripartite, generalized star, and wheel graphs. At the end, we propose some open problems.

Single Source Precursor Path to 2D Materials: A Case Study of Solution‐Processed Molybdenum‐Rich MoSe2‐x Ultrathin Nanosheets

AbstractTwo‐dimensional Molybdenum diselenide is a versatile and promising material used for a wide range of applications and its synthesis in high quality and yield is currently the subject of intense research. Low temperature solution phase synthesis of nanomaterials using designed molecular precursors enjoys tremendous advantages over traditional high‐temperature solid‐state synthesis. However, molecular precursor chemistry of molybdenum selenide nanomaterials is almost non‐existent. We report here synthesis and structural characterization of new molybdenum molecular precursors with selenoether and selenourea ligands, which due to their easy synthesis, desired Mo/Se composition and moderate processing properties, are highly promising as single source precursors for the scale‐up production of 2D molybdenum diselenide materials. This is demonstrated by the solution‐phase decomposition of the Mo‐selenourea precursor which produced Mo‐rich MoSe2‐x ultrathin nanosheets (NSs) in good yield. These NSs were characterized by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP‐OES), Powder X‐ray diffraction (XRD), Raman spectroscopy, Fourier Transform Infrared Spectroscopy (FT‐IR), Thermogravimetric analysis (TGA), Transmission electron microscopy (TEM) and X‐ray photoelectron spectroscopy (XPS) studies.

A molecular-chemistry-manipulation strategy toward functional lignin-based porous carbon nanospheres with tunable-pore structure

Porous carbon nanospheres derived from biomass materials have inspired numerous interests in various areas. Nevertheless, their fabrications mainly depend on template-assisted or chemical activation routes. Here, a molecular-chemistry-manipulation strategy was proposed innovatively by taking advantage of the heterogeneous self-condensation characters of lignin molecules under thermal stimulation, opening a practical and versatile synthetic platform for the construction of lignin-based hierarchical porous carbon nanospheres (LHPCS). We can manipulate the condensed kinetics by tuning the environmental variables and forming designated nanospheres with inhomogeneous molecular chemistry. This intraparticle difference could provide a possibility to tune its refined structure for synthesizing hierarchical porous carbon nanospheres. Such LHPCS behaved the specific surface area of 962 m2 g−1 and pore volume of 0.76 cm3 g−1, as well as regulable pore ratios (15.5–22.9 % of macropore, 12.7–37 % for mesopore and 45–68.9 % for micropore). Besides, the monodisperse spherical morphology with hierarchical interleaved channels affords them excellent performance in optical management and energy storage, including pore-determined photothermal properties, extremely low reflectance of ca. 1.9 %, stable multi-angles specular reflectance, and anti-self-discharge induced by charge rearrangement. Our proposed molecular chemistry strategy provides an alternative pathway for constructing gradient and adjustable pore structures.

Structure, orientation, and dynamics of per- and polyfluoroalkyl substance (PFAS) surfactants at the air-water interface: Molecular-level insights

HypothesisUnderstanding the intricate molecular-level details of toxic per- and polyfluoroalkyl substances (PFAS) partitioning to the air–water interface holds paramount importance in evaluating their fate and transport, as well as for finding safer alternatives for various applications, including aqueous film forming foams. The behavior of these substances at interfaces strongly depends on molecular architecture, chemistry, and concentration, which define molecular packing, self-assembly, interfacial diffusion, and the surface tension. SimulationsModeling of three PFAS surfactants, namely, longer-tail (perfluorooctanoate (PFOA)) and shorter-tail (perfluorobutanoate (PFBA) and 2,3,3,3-tetrafluoro-2-(heptafluoropropoxy) propanoate (GenX)) has been conducted using atomistic molecular dynamics simulations. A systematic comparison between these representative PFAS of different sizes and structure reveals factors influencing their association behavior, mechanism of surface tension reduction, and interfacial mobility as a function of surface coverage. FindingsShorter-chain PFAS surfactants (GenX or PFBA) require lower surface coverage compared to longer chain (PFOA) PFAS to achieve the same decrease in surface tension. However, a higher concentration of GenX and PFBA is necessary in the bulk aqueous solution to achieve the same surface coverage as PFOA, due to their higher solubility in water. The PFAS molecular orientation and mobility at the interface are found to be vastly influenced by the length and architecture of the hydrophobic fluorocarbon tail. A significant ordering of the water dipole moment near the anionic headgroup is apparent at high surface concentration. A direct correlation is established between the PFAS interfacial properties and PFAS-PFAS, PFAS-counterion, and PFAS-water interactions.

Synthesis and Reactivity of an Alumanyl-Tin Species to Form an Al,N-Heteroallene Derivative.

The molecular chemistry of metal-metal bonds is crucial for understanding the bonding and reactivity of metal-containing molecules as well as solid metals and their alloys, especially with respect to their application as catalysts for organic transformations and industrial processes. Despite the high efficiency of the recently developed nucleophilic alumanyl anions in the synthesis of diverse Al-metal bonds, the bonding between main group metals in a low oxidation state and the Al atom remains largely unexplored. This paper describes the reaction of an alumanyl anion with a chlorostannylene precursor to afford a compound with an unprecedented covalent Al-Sn bond. The lability of the covalent Al-Sn bond renders it reactive toward the insertion of carbodiimide, N2O, and phenylacetylene. Remarkably, the reaction with benzonitrile furnished an unprecedented Al,N-heteroallene species that contains a linear Al═N═C linkage.

Exploring the Role of Excited States' Degeneracy on Vibronic Coupling with Atomic-Scale Optics.

Interactions between molecular electronic and vibrational states manifest themselves in a variety of forms and have a strong impact on molecular physics and chemistry. For example, the efficiency of energy transfer between organic molecules, ubiquitous in biological systems and in organic optoelectronics, is strongly influenced by vibronic coupling. Using an approach based on scanning tunneling microscope-induced luminescence (STML), we reveal vibronic interactions in optical spectra of a series of single phthalocyanine derivative molecules featuring degenerate or near-degenerate excited states. Based on detailed theoretical simulations, we disentangle spectroscopic signatures belonging to Franck-Condon and Herzberg-Teller vibronic progressions in tip-position-resolved STML spectra, and we directly map out the vibronic coupling between the close-lying excited states of the molecules.

B-035 Atellica CI Analyzer Sample-to-sample Carryover Performance Evaluation

Abstract Background Laboratories in many parts of the world are required to demonstrate/confirm the lack of sample carryover on their analyzer under their laboratory conditions using patient populations. Sample carryover occurs when analyte adheres to the probe and/or cuvette and contaminates a subsequent sample or cuvette in the testing sequence of the analyzer. Inadequate washing of the sample aspiration/ dispensing device and cuvette can cause this type of carryover. The Atellica® CI Analyzer uses independent integration and random-access sampling to process chemistry (CH) and immunoassay (IM) tests. The immunoassays performed on the IM portion of the analyzer utilizes a single-use, disposable sample tip and reaction cuvette which reduces sample carryover from IM to CH tests, whereas a reusable probe is used for CH assays—which is washed between sampling. This study evaluated the potential for carryover of CH samples from one sample to the next due to washing protocols on the Atellica® CI Analyzer in four independent real world clinical laboratories. Methods Carryover verification was performed as per CLSI GP31-A:2009 and Vericheck protocols. The study was conducted at four sites (UAE, France, Croatia, U.S.) using 2 serum samples prepared in 12 sample containers-9 for low (L) and 3 for high (H) concentrations of analyte. For glucose (GluH_3), UAE, L and H were 63 and 356 mg/dL (3.5 and 19.8 mmol/L), Croatia, 77.4 and 237.5 mg/dL (4.3 and 12.2 mmol/L), and U.S., 101 and 374 mg/dL (5.6 and 20.8 mmol/L). For creatine kinase (CK_L), France, L and H were 40 and 1400 U/L. Samples were tested in a defined sequence: L1, L2, L3, H1, L4, H2, L5, H3, L6, L7, L8, L9 and results analyzed for % carryover. Carryover was defined as average of L4, L5, L6 minus the average of L1, L2, L3, L7, L8, L9 divided by the average of H1, H2, H3. Results No significant carryover was observed at any site. Carryover % for UAE, France, Croatia, and U.S was 0.500%, -0.085%, -0.095%, and -0.095%, respectively. Conclusions When using high positive samples at concentrations at or greater than the upper limit of the clinical range, negligible CH sample carryover was found from the Atellica CI Analyzers in four independent real world clinical laboratory settings.

Application of machine learning based genome sequence analysis in pathogen identification.

Infectious diseases caused by pathogenic microorganisms pose a serious threat to human health. Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious diseases remain a significant public health concern. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance requires concerted interdisciplinary efforts. With the development of computer technology and the continuous exploration of artificial intelligence(AI)applications in the biomedical field, the automatic morphological recognition and image processing of microbial images under microscopes have advanced rapidly. The research team of Institute of Microbiology, Chinese Academy of Sciences has developed a single cell microbial identification technology combining Raman spectroscopy and artificial intelligence. Through laser Raman acquisition system and convolutional neural network analysis, the average accuracy rate of 95.64% has been achieved, and the identification can be completed in only 5 min. These technologies have shown substantial advantages in the visible morphological detection of pathogenic microorganisms, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this review, we discuss the application of AI-based machine learning in image analysis, genome sequencing data analysis, and natural language processing (NLP) for pathogen identification, highlighting the significant role of artificial intelligence in pathogen diagnosis. AI can improve the accuracy and efficiency of diagnosis, promote early detection and personalized treatment, and enhance public health safety.

Development of polarizable and hydration-focused water models for the Martini 3 force field

Coarse-grained simulations have been increasingly employed in many fields of molecular chemistry. These models enable the evaluation of complex molecular structures by simplifying their description compared to all-atoms models. Specifically, the Martini 3 force field has been developed to describe biomolecular structures such as proteins, lipids, and carbohydrates. Their “building block” approach has been successfully used in modeling complex systems ranging from carbohydrate solutions, biological membranes, and even deep eutectic solvents. However, one main issue arises when considering interfacial systems: water is represented as a single particle with no effect of electrostatic interactions. In this work, we developed a polarizable water model for the Martini 3 force field, introducing charged particles to model the electrostatic interactions present in water systems accurately. Gaussian Processes were trained with simulation data for both bulk water and cross-interaction parameterization to consistently explore the parameter space and fine-tune our model to the target properties. The developed model showed good performance in reproducing the density and dielectric constant of bulk water. Regarding cross-interaction with other beads, two different sets of parameters were developed. The first, called the polarizable model, is parameterized to reproduce the hydration free energies of the original Martini 3 organic beads. This allows for a good description of free energies of transfer between water and organic phases, the main target property for the Martini 3 force field. The second, called the polarizable-hydration model, is parameterized to match the experimental hydration free energy of selected molecules. This allows for more accurate evaluation of hydration properties, which can be useful for water solutions. In particular, we show that the partitioning and interfacial behavior of nonionic surfactants is improved by the polarizable-hydration model when compared to the standard Martini 3 water model.

The Dustiest Galactic S Stars: Mid-infrared Spectra from SOFIA/FORCAST

We present spectra of 12 of the reddest, and hence dustiest, S stars in the Milky Way, observed with the FORCAST grisms on SOFIA. S stars are asymptotic giant branch (AGB) stars with C/O ∼ 1, so their molecular and dust chemistries are dominated by neither O nor C, often leading to atypical spectral features from their molecules and dust grains. All of the stars in our sample have strong dust emission features at 10–11 μm, but the shape of the feature in most of the stars differs from the shapes commonly observed in either oxygen-rich or carbon-rich AGB stars. Two stars also show the 13 μm feature associated with crystalline alumina. Two have a water absorption band at ∼6.5–7.5 μm, and a third has a tentative detection, but only one of these three has the more common SiO absorption band at 7.5 μm. Three others show a red 6.3 μm emission feature from complex hydrocarbons consistent with “Class C” objects, and in a fourth it appears at 6.37 μm, redder than even the standard Class C hydrocarbon feature. Class C spectra typically indicate complex hydrocarbons that have been less processed by UV radiation, resulting in more aliphatic bonds relative to aromatic bonds. None of the S stars show a strong 11.3 μm hydrocarbon feature, which is also consistent with the presence of aliphatic hydrocarbons.

Tunable Interlayer Interactions in Exfoliated 2D van der Waals Framework Fe(SCN)2(Pyrazine)2.

2D materials can be isolated as monolayer sheets when interlayer interactions involve weak van der Waals forces. These atomically thin structures enable novel topological physics and open chemical questions of how to tune the structure and properties of the sheets while maintaining them as isolated monolayers. Here, this work investigates 2D electroactive sheets that exfoliate in solution into colloidal nanosheets, but aggregate upon oxidation, giving rise to tunable interlayer charge transfer absorption and photoluminescence. This optical behavior resembles interlayer excitons, now intensely studied due to their long-lived emission, but which remain difficult to tune through synthetic chemistry. Instead, the interlayer excitons of these framework sheets can be modulated through control of solvent, electrolyte, oxidation state, and the composition of the framework building blocks. Compared to other 2D materials, these framework sheets display the largest known interlayer binding strengths, attributable to specific orbital interactions between the sheets, and among the longest interlayer exciton lifetimes. Taken together, this study provides a microscopic basis for manipulating long-range opto-electronic behavior in van der Waals materials through molecular synthetic chemistry.

Unlocking biomass valorization: Machine Learning insights for Reductive Catalytic Fractionation of cotton stalks

Lignocellulosic biomass valorization has become an intensive area of research due to the importance of renewable nature and availability of biomass. However, biomass fractionation and depolymerization produce numerous datasets, that are difficult to visualise and interpret for the scale-up of the process. Therefore, machine learning algorithms, which can discover hidden patterns in data are applied to these datasets. Reductive Catalytic Fractionation (RCF) of lignocellulosic biomass is an emerging methodology to valorize biomass completely and effectively. Herein, the present work includes the Correlation Analysis and the Principal Component Analysis (PCA) of product distribution obtained from RCF of cotton stalks. Interactions between process variables and delignification (DL), sugar retention (SR), total phenolic monomers (PM), and individual phenolic monomers yield were evaluated. Correlations among DL, SR, and PM yields were also evaluated at different reaction conditions through PCA, which were explained using the reaction mechanism and molecular chemistry of lignin.

Design, synthesis and neuroprotective activity of compound derived from Gastrodia elata Blume and borneol.

Traditional Chinese medicine Gastrodia elata Blume (GEB) possesses properties that soothe the liver and dispel wind. Its constituents exhibit numerous pharmacological properties, including neuroprotective effects, analgesic properties for headache relief, memory enhancement, and others. Borneol enhances drug absorption by traversing the blood-brain barrier, thereby improving its bioavailability and therapeutic efficacy. The research aimed to design innovative drug molecules and contribute to the beneficial exploration of compound Chinese medicine modernization. This study employed the strategy of "compound Chinese medicine molecular chemistry" to integrate and fuse the effective substances of compound Chinese medicines. An excitotoxic injury model was established by exposing PC12 cells to glutamate. Cell viability was quantitatively evaluated utilizing a colorimetric assay with the CCK-8 reagent kit. Genecards, Disgenet, and OMIM databases were used to identify potential disease-related targets. Molecular docking methods were performed to predict the binding interactions between compounds and core targets. We designed and synthesized compounds TB-1 to TB-16. Following the evaluation of their safety, TB-1, TB-2, TB-12, and TB-16 were selected for further investigation of their neuroprotective properties. The compound designed in this study exhibits a dose-dependent protective effect on glutamate-damaged PC12 cells. Further network pharmacology and molecular docking analyses indicate that TB-2 possesses a potential therapeutic effect against cerebral ischemia, and its possible targets were SRC, MAPK1 and KDR. The results indicated that TB-2 displayed a significant neuroprotective effect against Glu-induced injury in PC12 cells, suggesting potential therapeutic implications for cerebral ischemia.

Chemicals regulation and non-animal methods: displacing the gold standard.

Regulating industrial chemicals in foodstuffs and consumer products is a major aspect of protecting populations against health risks. Non-animal testing methods are an essential part of the radical change to the framework for toxicity testing that is long overdue in global economies. This paper discusses reasons why the drive to reduce animal testing for chemical safety testing is so difficult to achieve, as perceived by those who are closely involved in chemicals regulations in different capacities. Progress is slow, despite the fact that the ethico-legal conditions for a move away from animal testing are largely in place, and despite scientific arguments for a radical change in the paradigm of toxicity testing, away from reliance on animal studies. I present empirical data drawn from two studies in a European Commission context promoting non-animal methods. The aim of the paper is modest. It is to foreground the voices of those who deal with the science and regulation of chemicals on a day-to-day basis, rather than to offer a theoretical framework for what I heard from them. I offer a synthesis of the main challenges faced by non-animal alternatives, as these are perceived by people in different stakeholder groups dealing with chemicals regulation. I show where there are pockets of agreement between different stakeholders, and where the main disagreements lie. In particular there is dispute and disagreement over what counts as validation of these alternative tests, and by implication of the traditional 'gold standard' of animal testing. Finally, I suggest that the shift to non-animal methods in chemicals regulation demonstrates the need for the concept of validation to be broadened from a purely techno-scientific definition, and be more explictly understood as a demand for trust and acceptance, with more attention given to the complex social, institutional and economic settings in which it operates.

Optical Readout of Single-Molecule Magnets Magnetic Memories with Unpolarized Light.

Magnetic materials are widely used for many technologies in energy, health, transportation, computation, and data storage. For the latter, the readout of the magnetic state of a medium is crucial. Optical readout based on the magneto-optical Faraday effect was commercialized but soon abandoned because of the need for a complex circular polarization-sensitive readout. Combining chirality with magnetism can remove this obstacle, as chiral magnetic materials exhibit magneto-chiral dichroism, a differential absorption of unpolarized light dependent on their magnetic state. Molecular chemistry allows the rational introduction of chirality into single-molecule magnets (SMMs), ultimate nanoobjects capable of retaining magnetization. Here, we report the first experimental demonstration of optical detection of the magnetic state of an SMM using unpolarized light on a novel air-stable Dy-based chiral SMM featuring a strong single-ion magnetic anisotropy. These findings might represent a paradigm shift in the field of optical data readout technologies.

Solute structure effect on polycyclic aromatics separation from fuel oil: Molecular mechanism and experimental insights

AbstractIonic liquids (ILs) are promising solvents for separating aromatics from fuel oils. However, studies for separate polycyclic aromatics with ILs are rare and insufficient, and the impact of solute structure on extraction performance still needs to be determined. In this work, we use 1‐ethyl‐3‐methylimidazolium bis([trifluoromethyl]sulfonyl)imide ([EMIM][NTF2]) as an extractant to separate 1‐methylnaphthalene, quinoline, and benzothiophene from dodecane mixtures. Liquid–liquid equilibrium experiments identified the optimal operating conditions. Nine solute molecules, including five alkanes and four aromatic hydrocarbons, were used to study the relationship between extraction performance and solute structure. Molecular dynamics simulation and quantum chemistry calculations gave a deep insight and reasonable interpretation of the structure‐performance relationship at the molecular level. An industrial‐scale extraction process was proposed. The IL can be easily regenerated using heptane as a back‐extractive solvent. A high‐purity fuel oil with aromatic content below 0.5 wt% is obtained after 8‐stage extraction.

Pyridazine Derivatives: Molecular Docking, ADMET Prediction, and Synthesis for Antihypertensive Activity.

The drug discovery and development domain has witnessed remarkable advancements due to the integration of computational methods, particularly Computer-Aided Drug Design (CADD). Discovering and creating new drugs involves structural modifications to enhance their effectiveness and physical attributes. This frequently includes employing semisynthetic techniques to investigate structure-activity relationships thoroughly. Noticeable progress in molecular biology, computational chemistry, combinatorial chemistry, and highthroughput screening is steering transformative changes in the pharmaceutical industry. High blood pressure or hypertension, a significant health issue, elevates the chances of heart, kidney, and brain complications, among other health concerns. It's a leading cause of untimely mortality globally. Therefore, it is important to search for new antihypertensive compounds that have fewer side effects and higher therapeutic activity. Following molecular docking of the pyridazine derivatives, compounds were subjected to In-silico ADMET analysis. Subsequently, a low molecular weight compound was synthesized. Among the synthesized compounds characterization procedures include TLC, FT-IR, 1HNMR, and LC-MS techniques. Compound 8 exhibited the most favorable molecular docking results with alpha A1 and beta 1 adrenergic receptors. Compounds 3, 5, and 6 fulfilled the essential ADMET criteria. Subsequently, Compounds 3, 4, and 5 underwent additional synthesis and characterization procedures, including TLC, FT-IR, 1H-NMR, and LC-MS techniques. Similar behavior was observed in compounds 6, 8, 10, and 11, all violating Pfizer's 3/75 rules in terms of TPAS. Hydrazinolysis of these b-benzoyl propionic acids produced pyridazine, which was utilized in synthesizing pyridazine derivatives. TLC, FT-IR, 1HNMR, and LCMS have characterized the compounds.

PW-SMD: A Plane-Wave Implicit Solvation Model Based on Electron Density for Surface Chemistry and Crystalline Systems in Aqueous Solution.

Electron density-based implicit solvation models are a class of techniques for quantifying solvation effects and calculating free energies of solvation without an explicit representation of solvent molecules. Integral to the accuracy of solvation modeling is the proper definition of the solvation shell separating the solute molecule from the solvent environment, allowing for a physical partitioning of the free energies of solvation. Unlike state-of-the-art implicit solvation models for molecular quantum chemistry calculations, e.g., the solvation model based on solute electron density (SMD), solvation models for systems under periodic boundary conditions with plane-wave (PW) basis sets have been limited in their accuracy. Furthermore, a unified implicit solvation model with both homogeneous solution-phase and heterogeneous interfacial structures treated on equal footing is needed. In order to address this challenge, we developed a high-accuracy solvation model for periodic PW calculations that is applicable to molecular, ionic, interfacial, and bulk-phase chemistry. Our model, PW-SMD, is an extension of the SMD molecular solvation model to periodic systems in water. The free energy of solvation is partitioned into the electrostatic and cavity-dispersion-solvent structure (CDS) contributions. The electrostatic contributions of the solvation shell surrounding solute structures are parametrized based on their geometric and physical properties. In addition, the nonelectrostatic contribution to the solvation energy is accounted for by extending the CDS formalism of SMD to incorporate periodic boundary conditions. We validate the accuracy and robustness of our solvation model by comparing predicted solvation free energies against experimental data for molecular and ionic systems, carved-cluster composite energetic models of solvated reaction energies and barriers on surface systems, and deep-learning-accelerated ab initio molecular dynamics (AIMD). Our developed periodic implicit solvation model shows significantly improved accuracy compared to previous work (namely, solvation models in aqueous solution) and can be applied to simulate solvent effects in a wide range of surface and crystalline materials.

Advancing scanning probe microscopy simulations: A decade of development in probe-particle models

The Probe-Particle Model combine theories designed for the simulation of scanning probe microscopy experiments, employing non-reactive, flexible tip apices to achieve sub-molecular resolution. In the article we present the latest version of the Probe-Particle Model implemented in the open-source ppafm package, highlighting substantial advancements in accuracy, computational performance, and user-friendliness. To demonstrate this we provide a comprehensive review of approaches for simulating non-contact Atomic Force Microscopy. They vary in complexity from simple Lennard-Jones potential to the latest full density-based model. We compared those approaches with ab initio calculated references, showcasing their respective merits. All parts of the ppafm package have undergone acceleration by 1-2 orders of magnitude using OpenMP and OpenCL technologies. The updated package includes an interactive graphical user interface and seamless integration into the Python ecosystem via pip, facilitating advanced scripting and interoperability with other software. This adaptability positions ppafm as an ideal tool for high-throughput applications, including the training of machine learning models for the automatic recovery of atomic structures from nc-AFM measurements. We envision significant potential for this application in future single-molecule analysis, synthesis, and advancements in surface science in general. Additionally, we discuss simulations of other sub-molecular scanning-probe imaging techniques, such as bond-resolved scanning tunneling microscopy and kelvin probe force microscopy, all built on the robust foundation of the Probe-Particle Model. Altogether this demonstrates the broad impact of the model across diverse domains of on-surface science and molecular chemistry.