Molecular-level Insights Research Articles

Abstract A015: Design and deciphering of precision peptide inhibitors for cancer stemness using generative deep learning and molecular dynamics simulations

Abstract The use of artificial intelligence (AI) and machine learning (ML) for exploring vast amino acid sequence spaces has recently gained traction in the discovery of antibiotics and the design of biomaterials with a wide array of algorithms. However, despite some advancements, designing peptide inhibitors to specifically modulate protein-protein interactions remains a significant challenge. In this contribution, we explore the use of a Long Short-Term Memory (LSTM) network - a type of recurrent neural network - to model peptide sequences, given its ability to process sequential data and capture long-term dependencies, critical for peptide design. Our research focuses on developing precision peptides that target the interaction between the Notch intracellular domain (NICD) and CBF1/RBPJ transcription factors, key regulators of the Notch signaling pathway implicated in breast and pancreatic cancer stemness. We inferred hydrophobic, hydrophilic, van der Waals, and salt bridge interactions from experimentally determined three-dimensional protein complex structures, which were used for feature engineering via one-hot encoding. Peptides, each 20 amino acids in length, were generated using temperature scaling in the LSTM model. These peptides were then structurally optimized and subjected to molecular dynamics (MD) simulations, followed by molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) analysis to assess their interactions and binding affinities with the Notch receptor. The MD simulations provide valuable molecular-level insights into the peptide-Notch interactions, helping to evaluate their binding strength. Further biological testing is underway to validate the efficacy of these lead peptide inhibitors and elucidate their molecular mechanisms in targeting cancer stem cells associated with breast cancer. Citation Format: Gurudeeban Selvaraj, Satyavani Kaliamurthi, Gilles H. Peslherbe. Design and deciphering of precision peptide inhibitors for cancer stemness using generative deep learning and molecular dynamics simulations. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A015

Exploring the role of edges in surface functionalization and stability of plasma-modified carbon materials: Experimental and DFT insights

Effective surface functionalization of carbon nanomaterials plays a crucial role in various applications. We investigated the impact of edges on surface functionalization and stability of oxygen-modified carbon materials using a combination of experimental techniques and Density Functional Theory (DFT) insights. Graphenic paper, highly oriented pyrolytic graphite (HOPG), and graphenic flakes were employed as model systems, with oxygen plasma treatment (generator power 100 W, oxygen pressure 0.2 mbar, exposure time 6 – 300 s) serving as the modification method. Surface morphology and chemical composition were characterized using scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy. The results revealed the introduction of oxygen functional groups on the investigated carbon surfaces (up to 20 at % by XPS) whereas; the structural integrity of the materials remained intact upon plasma modification (SEM, Raman). Work function was used as a sensitive parameter for monitoring the surface changes (increase by ∼1.4 eV, 1.3 eV, and 1 eV for graphenic paper, HOPG, and graphenic flakes, respectively) while time-dependent measurements revealed distinct kinetic processes governing the decay of functionalization, highlighting the role of surface defects in post-plasma processes. DFT calculations provided molecular-level insights into the surface processes, elucidating the mechanisms underlying the diffusion of hydroxyls, their recombination, and water desorption. Since the calculated activation barrier for recombination on basal graphenic planes (∼1.0 eV) and edges (∼5.5 eV) are distinctly different, it can be thus concluded that the persistent functionalization is due to the surface edges. Our findings contribute to a deeper understanding of surface modification processes of carbon materials and offer rationales for the design of advanced functional nanomaterials with tailored surface properties.

Unraveling electrocatalyst reaction mechanisms in water electrolysis: In situ Raman spectra

Electrocatalysis is crucial for sustainable energy solutions, focusing on energy harvesting, storage, and pollution control. Despite the development of various electrocatalysts, understanding the dynamic processes in electrochemical reactions is still limited, hindering effective catalyst design. In situ Raman spectra have emerged as a critical tool, providing molecular-level insights into surface processes under operational conditions and discussing their development, advantages, and configurations. This review emphasizes new findings at the catalyst–electrolyte interface, especially interface water molecule state, during the hydrogen evolution reaction and oxygen evolution reaction in recent years. Finally, the challenges and future directions for in situ Raman techniques in electrocatalysis are discussed, emphasizing their importance in advancing understanding and guiding novel catalyst design.

Tracking dendrites and solid electrolyte interphase formation with dynamic nuclear polarization-NMR spectroscopy.

Polymer-ceramic composite electrolytes enable safe implementation of Li metal batteries with potentially transformative energy density. Nevertheless, the formation of Li-dendrites and its complex interplay with the Li-metal solid electrolyte interphase (SEI) remain a substantial obstacle which is poorly understood. Here we tackle this issue by a combination of solid-state NMR spectroscopy and Overhauser dynamic nuclear polarization (DNP) which boosts NMR interfacial sensitivity through polarization transfer from the metal conduction electrons. We achieve detailed molecular-level insight into dendrites formation and propagation within the composites and determine the composition and properties of their SEI. We find that the dendrite's quantity and growth path depend on the ceramic content and correlated with battery's lifetime. We show that the enhancement of Li resonances in the SEI occurs through Li/Li+ charge transfer in Overhauser DNP, allowing us to correlate DNP enhancements and Li transport and directly determine the SEI lithium permeability. These findings have promising implications for SEI design and dendrites management which are essential for the realization of Li metal batteries.

Molecular level insights on the pulsed electrochemical CO2 reduction

Electrochemical CO2 reduction reaction (CO2RR) occurring at the electrode/electrolyte interface is sensitive to both the potential and concentration polarization. Compared to static electrolysis at a fixed potential, pulsed electrolysis with alternating anodic and cathodic potentials is an intriguing approach that not only reconstructs the surface structure, but also regulates the local pH and mass transport from the electrolyte side in the immediate vicinity of the cathode. Herein, via a combined online mass spectrometry investigation with sub-second temporal resolution and 1-dimensional diffusion profile simulations, we reveal that heightened surface CO2 concentration promotes CO2RR over H2 evolution for both polycrystalline Ag and Cu electrodes after anodic pulses. Moreover, mild oxidative pulses generate a roughened surface topology with under-coordinated Ag or Cu sites, delivering the best CO2-to-CO and CO2-to-C2+ performance, respectively. Surface-enhanced infrared absorption spectroscopy elucidates the potential dependence of *CO and *OCHO species on Ag as well as the gradually improved *CO consumption rate over under-coordinated Cu after oxidative pulses, directly correlating apparent CO2RR selectivity with dynamic interfacial chemistry at the molecular level.

Identification of Potential Inhibitors of Mycobacterium tuberculosis Amidases: An Integrated In Silico and Experimental Study.

Virtual screening is a crucial tool in early stage drug discovery for identifying potential hit candidates. Here, we present an integrated approach that combines theoretical and experimental techniques to identify, for the first time, inhibitors of amidases (Ami1-Ami4) from Mycobacterium tuberculosis. Through computational methods, we proposed a set of potential inhibitors, which were subsequently evaluated experimentally using differential scanning fluorimetry. This led to the identification of two promising hits: a carbohydrazide core (hit 1) and a tetrazole core (hit 5). We further developed a small collection of compounds derived from hit 1, which demonstrated improved affinity for Ami1. Additionally, we determined the crystallographic structure of the Ami1-hit 5 complex at a resolution of 1.45 Å, providing molecular-level insights into the interaction of this compound within the catalytic site. The findings of this study contribute to the advancement of drug discovery against tuberculosis and propose new targets for therapeutic development.

Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches.

The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin's complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.

Coiled Coil Peptide Tiles (CCPTs): Expanding the Peptide Building Block Design with Multivalent Peptide Macrocycles.

Owing to their synthetic accessibility and protein-mimetic features, peptides represent an attractive biomolecular building block for the fabrication of artificial biomimetic materials with emergent properties and functions. Here, we expand the peptide building block design space through unveiling the design, synthesis, and characterization of novel, multivalent peptide macrocycles (96mers), termed coiled coil peptide tiles (CCPTs). CCPTs comprise multiple orthogonal coiled coil peptide domains that are separated by flexible linkers. The constraints, imposed by cyclization, confer CCPTs with the ability to direct programmable, multidirectional interactions between coiled coil-forming "edge" domains of CCPTs and their free peptide binding partners. These fully synthetic constructs are assembled using a convergent synthetic strategy via a combination of native chemical ligation and Sortase A-mediated cyclization. Circular dichroism (CD) studies reveal the increased helical stability associated with cyclization and subsequent coiled coil formation along the CCPT edges. Size-exclusion chromatography (SEC), analytical high-performance liquid chromatography (HPLC), and fluorescence quenching assays provide a comprehensive biophysical characterization of various assembled CCPT complexes and confirm the orthogonal colocalization between coiled coil domains within CCPTs and their designed on-target free peptide partners. Lastly, we employ molecular dynamics (MD) simulations, which provide molecular-level insights into experimental results, as a supporting method for understanding the structural dynamics of CCPTs and their complexes. MD analysis of the simulated CCPT architectures reveals the rigidification and expansion of CCPTs upon complexation, i.e., coiled coil formation with their designed binding partners, and provides insights for guiding the designs of future generations of CCPTs. The addition of CCPTs into the repertoire of coiled coil-based building blocks has the potential for expanding the coiled coil assembly landscape by unlocking new topologies having designable intermolecular interfaces.

Structural and dynamical insights revealed the anti-glioblastoma potential of withanolides from Withania coagulans against vascular endothelial growth factor receptor (VEGFR).

Glioblastoma (GBM), well known as grade 4 tumors due to its progressive malignant features such as vascular proliferation and necrosis, is the most aggressive form of primary brain tumor found in adults. Mutations and amplifications in the vascular endothelial growth factor receptor (VEGFR) contribute to almost 25% of GBM tumors. And thus, VEGFR has been declared the primary target in glioblastoma therapeutic strategies. However, many studies have been previously reported that include GBM as global therapeutics challenge, but they lack the molecular level insights that could help in understanding the biological function of a therapeutically important protein playing a major role in the disease and design the best strategies to develop the potential drugs. Therefore, to the best of our knowledge, the present study is the first time of kind, which involves multi-in silico approaches to predict the inhibition potential of withanolides from Withania coagulan against VEGFR. The study is actually based on determining the mode of action of five isolates: withanolide J, withaperuvin, 27-hydroxywithanolide I, coagule E, and coagule E, along with their respective binding energies. Molecular docking simulations revealed primarily four ligands, withanolide J (- 7.33kJ/mol), 27-withanolide (- 7.01kJ/mol), ajugine, withaperuvin (- 6.89kJ/mol), and ajugine E (- 6.39kJ/mol), to have significant binding potencies against the protein. Ligand binding was found to enhance the confirmational stability of the protein revealed through RMSD analysis, and RMSF assessment revealed the protein residues especially from 900-1000 surrounding the binding of the protein. Structural and dynamics of the protein via dynamics cross-correlation movement (DCCM) and principal component analysis (PCA) in both the unbound form and complexed with most potent ligand, withanolide J, reveal the ligand binding affecting the entire conformational integrity of the protein stabilized by hydrogen bonds and electrostatic attractions. Free energy of binding estimations by means of molecular mechanics Poisson-Boltzmann surface area (MMPBSA) method further revealed the withanolide J to have maximum binding potency of the all ligands. Withanolide J in final was also found to have suitable molecular characterizations to cross the blood-brain barrier (BBB +) and reasonable human intestinal absorption ability determined by ADMET profiling via admetSAR tools.

Insights into the Synergistic Interplay of Ligand and Base Effects in Palladium-Catalyzed Enantioselectivity Hydrofunctionalization of Dienes.

Transition-metal-catalyzed enantioselective C-O bond constructions via hydrofunctionalization involving the use of O-based nucleophiles are an important topic in synthetic chemistry. Herein, density functional theory calculations were conducted to unveil the mechanism and enantioselectivity of Pd-catalyzed asymmetric hydrofunctionalization of conjugated dienes. We found that the base-assisted 4,3-activation model of the ligand-to-ligand hydrogen transfer (LLHT) mechanism is the most preferred one among all the cases, which could be ascribed to the favorable C-H···O interactions and the electrostatic interactions. For the enantioselective C-O bond formation process, the orientation of the substrate in the chiral pocket plays a significant role in controlling the enantioselectivity by contributing different noncovalent interactions. On the basis of the distortion/interaction model and energy decomposition analysis, the distortion energy is identified as the dominant factor controlling the product chemoselectivity. BnOH acts as the substrate, proton shuttle, and stabilizer to facilitate the H-transfer process in both LLHT and the C-O bond formation process. This study provides molecular-level insights into the collaborative effect of the base and P,N-ligand to perform the catalytic activity in the asymmetric hydrofunctionalization of conjugated dienes, which might open a new avenue for designing more efficient base-assisted enantioselective hydrofunctionalization by Pd catalysis.

Assessing the effect of deep eutectic solvents on α-chymotrypsin thermal stability and activity.

Optimizing the liquid reaction phase holds significant potential for enhancing the efficiency of biocatalytic pro- cesses since it determines reaction equilibrium and kinetics. This study investigates the influence of the addition of deep eutectic solvents on the stability and activity of α-chymotrypsin, a proteolytic enzyme with industrial rele- vance. Deep eutectic solvents, composed of choline chloride or betaine mixed with glycerol or sorbitol, were added in the reaction phase at various concentrations. Experimental techniques, including kinetic and fluorometry, were employed to assess the α-chymotrypsin activity, thermal stability, and unfolding reversibility. Atomistic molecular dynamics simulations were also conducted to assess the interactions and provide molecular-level insights between α-chymotrypsin and the solvent. The results showed that among all studied mixtures, adding choline chloride + sorbitol improved thermal stability up to 18 °C and reaction kinetic efficiency up to two-fold upon adding choline chloride + glycerol. Notably, the choline chloride + sorbitol system exhibited the most substantial stabilization effect, attributed to the surface preferential accumulation of sorbitol, as corroborated by the computational anal- yses. This work highlights the potential of tailoring liquid reaction phase of α-chymotrypsin catalyzed reaction using neoteric solventsto enhance α-chymotrypsin performance and stability in industrial applications.

Assessing vitamin E acetate as a proxy for E-cigarette additives in a realistic pulmonary surfactant model

Additives in vaping products, such as flavors, preservatives, or thickening agents, are commonly used to enhance user experience. Among these, Vitamin E acetate (VEA) was initially thought to be harmless but has been implicated as the primary cause of e-cigarette or vaping product use-associated lung injury, a serious lung disease. In our study, VEA serves as a proxy for other e-cigarette additives. To explore its harmful effects, we developed an exposure system to subject a pulmonary surfactant (PSurf) model to VEA-rich vapor. Through detailed analysis and atomic-level simulations, we found that VEA tends to cluster into aggregates on the PSurf surface, inducing deformations and weakening its essential elastic properties, critical for respiratory cycle function. Apart from VEA, our experiments also indicate that propylene glycol and vegetable glycerin, widely used in e-liquid mixtures, or their thermal decomposition products, alter surfactant properties. This research provides molecular-level insights into the detrimental impacts of vaping product additives on lung health.

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.

Roles of kaolinite-oil-gas molecular interactions in hydrogen storage within depleted reservoirs

Hydrogen is a clean alternative to fossil fuels, emitting only water vapor during combustion. In a future hydrogen economy, large-scale storage will be an important component of the supply chain. Due to its low volumetric density, conventional surface storage methods are inadequate. Underground hydrogen storage (UHS) offers a viable solution, enabling the storage of millions of cubic meters. Among potential geological sites, including salt caverns, aquifers, and depleted gas reservoirs, depleted oil reservoirs show promise. Studying hydrogen interactions with residual oil and reservoir minerals is vital for understanding the properties of hydrogen and the reservoir post-injection. In this study, we employed molecular dynamics simulations to gain molecular-level insights into these interactions. We investigated hydrogen dissolution in oil, adsorption at kaolinite/oil interfaces, the role of CO2 as a cushion gas, and the influence of kaolinite’s hydrophobicity on H2 behavior. The main findings include: (1) hydrogen dissolves more in oil than in water, (2) the introduction of CO2 suppresses hydrogen dissolution in oil and reduces the interfacial tension (IFT) between oil and gas, (3) CO2 decreases H2 partitioning near kaolinite surfaces due to its strong affinity for the hydrophilic gibbsite surface of kaolinite, and (4) CO2 is more effective than H2 in reducing IFT between kaolinite and the oil–gas mixture. These findings emphasize the effectiveness of CO2 as a cushion gas and the important role of clay hydrophobicity in UHS, providing insights that are challenging to obtain experimentally.

Coarse-Grained Simulations of Adeno-Associated Virus and Its Receptor Reveal Influences on Membrane Lipid Organization and Curvature.

Adeno-associated virus (AAV) is a well-known gene delivery tool with a wide range of applications, including as a vector for gene therapies. However, the molecular mechanism of its cell entry remains unknown. Here, we performed coarse-grained molecular dynamics simulations of the AAV serotype 2 (AAV2) capsid and the universal AAV receptor (AAVR) in a model plasma membrane environment. Our simulations show that binding of the AAV2 capsid to the membrane induces membrane curvature, along with the recruitment and clustering of GM3 lipids around the AAV2 capsid. We also found that the AAVR binds to the AAV2 capsid at the VR-I loops using its PKD2 and PKD3 domains, whose binding poses differs from previous structural studies. These first molecular-level insights into AAV2 membrane interactions suggest a complex process during the initial phase of AAV2 capsid internalization.

Assessment of dissociation enthalpies of methane hydrates in the absence and presence of ionic liquids using the Clausius-Clapeyron approach

The Clausius-Clapeyron (CC) equation is generally preferred to obtain dissociation enthalpies (ΔH) of hydrate-forming systems due to its ease of use. The application of other direct and indirect methods becomes more problematic if complex additives such as ionic liquids (ILs) are also present in the system. In this work, around 400 equilibrium data points for methane hydrates in the presence of over 80 ILs were collected from the literature in the temperature and pressure ranges of (272.10 – 306.07) K and (2.48 – 100.34) MPa, respectively. The ΔH of methane hydrates in the absence and presence of ionic liquids (ILs) have been calculated using the CC equation. The compressibility factor (z), required to calculate ΔH at each phase equilibrium condition has been obtained from three different approaches viz., Peng-Robinson (PR) equation of state, Soave-Redlich-Kwong (SRK) equation of state and Pitzer (Pz) correlation. The results were compared to the experimentally reported dissociation enthalpy (54.5 ± 1.5 kJ.mol−1) of methane hydrates. The role of the compressibility factor along with the slope of the equilibrium data set and the temperature/pressure range in determining the outcome of the CC equation has been discussed. The effect of molar mass, molar volume, and hydrate suppression temperature of the ILs on the ΔH of methane hydrates has been explored. The tested ILs do not show a systematic and significant influence on enthalpies, rather they show a large scattering in the ΔH values, which might mask any existing subtle effect of the ILs on the dissociation enthalpies. Therefore, this approach should be dealt with care to obtain molecular-level insights.

Impact of polymer molecular weight and dispersity on the coalescence of polypropylene powders suitable for laser powder bed fusion

Understanding and controlling the fundamental processes governing the coalescence of polymers is vital to enable the design of polymeric materials for improved mechanical performance and quality of manufactured structures, including those fabricated via additive manufacturing techniques. Our group has utilized thermally induced phase separation (TIPS) to produce spherical and size controlled polypropylene (PP) powders from 12,000 (12k), 250,000 (250k), and 340,000 (340k) molecular weight (Mw) PP, including 50-50 wt% blends of 12k/250k,12k/340k, and 250k/340k, and a 33-33-33 wt% blend of 12k/250k/340k to investigate the impact of polymer Mw, and thus zero-shear viscosity, on particle coalescence and its implication in laser powder bed fusion. The particles exhibit similar size distributions with an average particle size, Dx(50), in the range of 58 μm–86 μm. The coalescence behavior of the powders, evaluated via hot-stage microscopy, show that adding 12k PP in the blend significantly alters the coalescence dynamics of the 250k and 340k PP, dramatically increasing their coalescence rate. The substantial drop in zero-shear viscosity with addition of 12k PP provides the driving force for the pronounced enhancement in coalescence. Strong agreement between experimental results and the Hopper model of coalescence is observed only if corrected for extensional flow, exemplifying the importance of extensional flow on the coalescence process. The results also indicate that the 12k PP in the blend does not surface segregate in the TIPS process, but is homogeneously distributed in the blend. More broadly, these results provide molecular-level insight into how control of powder molecular weight characteristics and viscosity can offer pathways to optimize the consolidation of particles in manufacturing processes, including laser powder bed fusion.

Molecular insights into the synergistic inhibition mechanisms of antifreeze protein and methanol on carbon dioxide hydrate growth

The formation of CO2 hydrates during CO2 transportation and deep-sea injection poses a significant risk of pipeline blockage. Combining kinetic (KHIs) and thermodynamic hydrate inhibitors (THIs) serves as a promising efficient and economical strategy to mitigate this issue, whose performance and microscopic mechanisms yet are still poorly understood. This study employs molecular dynamics simulations to explore the effects of the combination of winter flounder antifreeze protein (wf-AFP) as a KHI and methanol as a THI on CO2 hydrate growth. We find that methanol and wf-AFP exhibit an intriguing synergistic inhibition performance on hydrate growth. In the AFP-only system, AFP serves as a spatial hindrance near the hydrate growth interface. However, in the AFP + methanol system, AFP changes its position due to the attractive force of methanol. Part of the AFP fragment remains on the hydrate interface, while the rest surrounds the CO2 droplet. Methanol, in addition to disrupting the water structure, combines with AFP to act as a double barrier to CO2 dissolution into the aqueous solution, thereby significantly reducing the gas source for hydrate growth. These findings provide molecular-level insights into hydrate inhibition mechanisms and guide the design of efficient inhibitors for safe CO2 transportation and injection.

Water structures and anisotropic dynamics at Pt(211)/water interface revealed by machine learning molecular dynamics

Abstract Water molecules at solid–liquid interfaces play a pivotal role in governing interfacial phenomena that underpin electrochemical and catalytic processes. The organization and behavior of these interfacial water molecules can significantly influence the solvation of ions, the adsorption of reactants, and the kinetics of electrochemical reactions. The stepped structure of Pt surfaces can alter the properties of the interfacial water, thereby modulating the interfacial environment and the resulting surface reactivity. Revealing the in situ details of water structures at these stepped Pt/water interfaces is crucial for understanding the fundamental mechanisms that drive diverse applications in energy conversion and material science. In this work, we have developed a machine learning potential for the Pt(211)/water interface and performed machine learning molecular dynamics simulations. Our findings reveal distinct types of chemisorbed and physisorbed water molecules within the adsorbed layer. Importantly, we identified three unique water pairs that were not observed in the basal plane/water interfaces, which may serve as key precursors for water dissociation. These interfacial water structures contribute to the anisotropic dynamics of the adsorbed water layer. Our study provides molecular-level insights into the anisotropic nature of water behavior at stepped Pt/water interfaces, which can influence the reorientation and distribution of intermediates, molecules, and ions—crucial aspects for understanding electrochemical and catalytic processes.

Thermomechanical motion in single crystals triggered by de-solvation induced structural transformation: Molecular insights and actuation

Stimuli-responsive molecular crystals have interesting applications as actuators and energy-harvesting materials. Herein, we report a nitrobenzoate stabilized nitropentaamminecobalt(III) complex (1H), exhibits thermo-mechanical motion ∼120 °C. During the process, the crystal converts collective molecular displacements within the lattice into a macroscopic movement due to dehydration-induced structural transformation (monoclinic-orthorhombic) forming a partially dehydrated species (1D) which was confirmed by single-crystal-to-single-crystal (SCSC) analysis. The dehydration process can be reversed when 1D was placed in moist air (RH > 80 %) reforming 1H. A three-state stability model analysis based on DFT calculations suggests that de-solvation of 1H leads to a large instability in the system, resulting in an intermediate state (1-int), which undergoes spontaneous relaxation through the movement of cations and anions forming 1D. Macroscopic transfer of thermo-mechanical energy from crystal to composite films was successfully established by fabrication of a hybrid polymer composite using 1H and polyvinyl di-fluoride (PVDF) that shows excellent actuating behavior upon heating. The present work provides a detailed experimental and theoretical molecular-level insights into thermo-mechanical motion and thereby brings a new dimension towards designing stimuli-responsive crystals as promising actuators.