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At least 829 records · Page 46

Simulation of metal nanoparticles growth in methane atmosphere of arc discharge: comparison to experiment

A direct current arc discharge in a methane atmosphere is a scalable and sustainable method to produce metal-carbon core–shell nanoparticles and single-walled carbon nanotubes, where a metal catalyst can be continuously supplied through evaporation of an anode made from the catalyst material. The size of catalyst particles is of critical importance as it can affect the synthesis yield and properties of nanotubes and core–shell nanoparticles. This study presents a numerical model describing the formation and growth of metal particles for the conditions representative of the arc discharge with an evaporating iron anode at near-atmospheric pressure of a methane-rich atmosphere. The model incorporates carbon adsorption to the metal surface and explains the limiting effect of carbon coverage on the size of metal nanoparticles. The predicted particle sizes are compared with experimental observations. The model also predicts higher concentrations of metal particles with the increasing partial pressure of methane.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Organizing Chaos: Boosting Thermoelectric Properties by Ordering the Clathrate Framework of Ba 8 Cu 16 As 30

The type I clathrate, Ba 8 Cu 16 As 30 , is reinvestigated and found to have a low-temperature polymorph mP 108-Ba 8 Cu 16 As 30 with ordered Cu and As sites. In situ temperature-dependent powder X-ray diffraction experiments guided synthetic efforts toward the synthesis of the ordered monoclinic ( mP 108) and disordered cubic ( cP 54) polymorphs with high phase purity. While a transition from mP 108-Ba 8 Cu 16 As 30 to cP 54-Ba 8 Cu 16 As 30 is not directly observed, cP 54-Ba 8 Cu 16 As 30 is stabilized through quenching from high temperatures and is confirmed through high-resolution synchrotron powder X-ray diffraction. Further, combined theoretical predictions and experimental observations of the thermoelectric properties of both polymorphs reveal that the ordering of Cu and As atoms in the clathrate framework simultaneously enhances the Seebeck coefficient and electronic conductivity by increasing the hole effective mass and reducing the electronic scattering events. Consequently, the zT of mP 108-Ba 8 Cu 16 As 30 reaches a maximum of 0.2 at 575 K, an order of magnitude higher than that of cP 54-Ba 8 Cu 16 As 30 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Data Science Shows that Entropy Correlates with Accelerated Zeolite Crystallization in Monte Carlo Simulations

We have performed a data science study of Monte Carlo simulation trajectories to understand factors that can accelerate formation of zeolite nanoporous crystals, a process that can take days or even weeks. In previous work, Monte Carlo simulations predicted and experiments confirmed that using a secondary organic structure-directing agent (OSDA) accelerates crystallization of all-silica LTA zeolite, with experiments finding a three-fold speedup [PCCP 24, 142-148 (2022)]. However, it remains unclear what physical factors cause the speed-up. Here, we apply data science to analyze the simulation trajectories to discover what drives accelerated zeolite crystallization in Monte Carlo going from a one-OSDA synthesis (1OSDA) to a two-OSDA version (2OSDA). We encoded simulation snapshots using the Smooth Overlap of Atomic Positions approach, which represents all 2- and 3-body correlations within a given cutoff distance. Principal component analyses failed to discriminate datasets of structures from 1OSDA and 2OSDA simulations, while the Support Vector Machine (SVM) approach succeeded at classifying such structures with an area-under-curve (AUC) score of 0.99 (where AUC = 1 is a perfect classification) with all 3-body correlations, and as high as 0.94 with only 2-body correlations. SVM decision functions reveal relatively broad / narrow histograms for 1OSDA / 2OSDA datasets, suggesting that the two simulations differ strongly in information heterogeneity. Informed by these results, we performed pair (2-body) entropy calculations during crystallization, resulting in entropy differences that semi-quantitatively account for the speedup observed in the previous Monte Carlo simulations. We conclude that altering synthesis conditions in ways that substantially changes the entropy of labile silica networks may accelerate zeolite crystallization, and we discuss possible approaches for achieving such acceleration.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Adaptive Computing for Scale-Up Problems

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the allocation adaptive to some objective such as optimization or model training. The framework enables the characterization and management of uncertainties associated with predictive models of complex systems when scale-up questions lead to significant model extrapolation. A key advancement of this framework is its integration of multi-fidelity surrogate modeling, uncertainty management, and automated orchestration of various computing and experimentation resources into a single integrated software package. This enables efficient multi-fidelity modeling across multiple computing resources by incorporating real-world constraints such as relative queue times and throughput on individual machines into the multi-fidelity sampling decision. We discuss applications of this framework to problems in the renewable energy space, including biofuels production, material synthesis, perovskite crystal growth, and building electrical loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Plumbene: a comprehensive review of theoretical and experimental advances

Plumbene, a two-dimensional (2D) material consisting of a single layer of lead (Pb) atoms in a honeycomb lattice, stands as a pioneering addition to the elemental 2D material family. Here, this comprehensive review encapsulates the theoretical predictions and experimental advancements that underscore its potential. With properties shaped by significant spin-orbit coupling and structural buckling, plumbene offers a playground for phenomena like the quantum spin Hall effect, superconductivity, and topological phase transitions. Despite promising theoretical insights, experimental realization has encountered challenges, primarily due to high reactivity of Pb and the need for precise fabrication conditions. This paper critically examines the electronic, thermal, mechanical, and topological attributes of plumbene, alongside synthesis methods, functionalization techniques, and substrate effects. It further proposes directions for addressing synthesis challenges and integrating plumbene into advanced technologies, particularly in electronic, spintronic, and quantum computing applications. This review aims to bridge the gap between theoretical potential and experimental progress, fostering a deeper understanding of this intriguing material.

2D materials↗

Enhancing dimensionality prediction in hybrid metal halides via feature engineering and class-imbalance mitigation

We present a machine learning (ML) framework for predicting the structural dimensionality of hybrid metal halides (HMHs), including organic-inorganic perovskites, using a combination of chemically-informed feature engineering and advanced class-imbalance handling techniques. This study is motivated by the small and highly imbalanced nature of experimentally available HMH datasets, which limits the applicability and reliability of conventional ML approaches. The dataset, consisting of 494 HMH structures, is highly imbalanced across dimensionality classes (0D, 1D, 2D, 3D), posing significant challenges to predictive modeling. To mitigate this limitation, the dataset was augmented to 1336 samples using the synthetic minority oversampling technique, enabling improved learning of underrepresented dimensionality classes while preserving chemically meaningful feature relationships. We developed interaction-based descriptors designed to capture coupled steric and polarity effects relevant to dimensionality prediction, which are not readily captured by standard single-parameter or composition-only descriptors. These descriptors are integrated into a multi-stage workflow combining feature selection, ensemble stacking, and performance optimization. Our approach significantly improves F1-scores for underrepresented classes, achieving robust cross-validation performance across all dimensionalities. This work demonstrates a generalizable strategy for extracting reliable and interpretable structure–dimensionality relationships from limited experimental data, enabling pre-synthesis screening of organic cations and providing a practical blueprint for small-data ML in hybrid materials systems.

36 MATERIALS SCIENCE↗

Foundations of Molecular 'Isotomics'

The naturally occurring rare isotopes are versions of common elements, such as hydrogen, carbon and oxygen, that contain a larger than usual number of neutrons in their atomic nuclei and therefore are higher in mass than the common atoms of that element. Isotopes exist for most elements and are found in most natural and synthetic materials, but are uneven in their distribution because chemical and physical processes are isotope-selective (e.g., a chemical reaction may proceed more rapidly for one isotope than for another). For this reason, abundances of isotopes in a material of interest can provide a record, or ‘signature’ of various features of that material’s origin and history. These signatures have been used in the geo, life, chemical and physical sciences in a wide variety of ways over close to 8 decades. However, many such applications struggle to reach unique interpretations of isotopic data because multiple factors combine to control a given sample’s overall isotopic content. That is, the factors controlling isotopic content are too numerous and complex to fully constrain from a simple measurement of a material’s isotope abundances. However, the distribution of isotopes within materials, at molecular scales potentially provides a vastly larger number and diversity of constraints on the chemical and physical processes that comprise a material’s history. The rare isotopes may be concentrated into one atomic position in a molecule relative to another, some proportion of molecules in a sample may contain two or more rare isotopes, and those multiply-isotope-substituted forms of molecules may also have uneven distributions of those isotopes across individual atomic sites. For these reasons, even small, seemingly simple molecules, such as sugars, amino acids or drug compounds, actually exist in a vast number of isotopically unique forms (often millions or more), and each one of those forms is in some sense an independent ‘vote’ on that sample’s history. This project has focused on opening this rich archive of information by enabling the creation of routinely and widely applicable ways of measuring and interpreting isotopic structures of molecules. This work has included the development of core technologies and analytical methods, advancing fundamental understanding of the physical and chemical properties of isotopic versions of molecules, and conducting proof of concept studies of illustrative geochemical, cosmochemical and forensic problems in order to show how these technologies, methods and principles come together to solve problems in new ways. A key to the success of this project was the adaptation of ‘Fourier transform mass spectrometry’ (FTMS) to the task of precisely measuring proportions of the rare, naturally occurring isotopic forms of molecules. FTMS is a highly specialized form of mass spectrometry that traps ions within magnetic or electrostatic cavities and, effectively, ‘listens’ (through registering of subtle electrical signals) to the harmonic signals they make while rapidly orbiting within those cavities. These signals have periods that are a function of their mass and strength (or ‘loudness’) that is proportional to their abundances. Thus, these signals constrain relative amounts of molecules that differ in their mass due to various isotopic substitutions. This technology has been essential to the identification of organic molecules in the life, chemical and environmental sciences for over 4 decades, but generally has lacked the control, stability and precision to meaningfully measure rare isotope forms of molecules. This project’s most fundamental contribution has been to modify FTMS, both in terms of hardware and methods, to enable such measurements. The raw data of molecular isotopic structure is tremendously voluminous and complex, so another important activity of this project has been developing the theoretical and data-science tools needed to interpret the data generated by this new form of isotopic measurement. A particularly challenging part of this task has been predicting molecular isotopic structure, as only through the comparison of measurements with predictions can we make progress on hypothesis driven research questions. We have attacked this this prediction task through a combination of first-principles chemical-physics models of the effects of isotope substitution on molecule properties and data-science models that permit us to generalize that chemical physics to cases that have not yet been studied by detailed chemical physics theory. The proof of concept applications we have pursued over the course of this study include biological reactions of amino acids and other biomolecules, non-biological synthesis of organic molecules in extra-terrestrial settings such as meteorites, petroleum geoscience questions concerning the origin and evolution of natural gas, oil and kerogen compounds, and forensic questions such as the sourcing of chemical weapons. The successes of these applications have laid the groundwork for the next phase of this field’s development, which will include larger scale and more ambitious studies of molecular isotopic structure as a means of diagnosing human diseases, such as cancer, and reconstructing detailed interpretations of the origin and evolution of organic molecules in modern and geological environments.

Cesar, Jaime↗

Chemistry of the outer planets

Various aspects were studied of past or present chemistry in the atmospheres of the outer planets and their satellites using lab simulations. Three areas were studied: (1) organic chemistry induced by kinetically hot hydrogen atoms in the region of Jupiter's atmosphere containing the ammonia cirrus clouds; (2) the conversion of NH3 into N2 by plasmas associated with entry of meteors and other objects into the atmosphere of early Titan; and (3) the synthesis of simple hydrocarbons and HCN by lightning in mixtures containing N2, CH4, and NH3 representing the atmospheres of Titan and the outer planets. The results showed that: (1) hot H2 atoms formed from the photodissociation of NH3 in Jupiter's atmosphere could account for some of the atmospheric chemistry in the ammonia cirrus cloud region; (2) the thermalization of hot H2 atoms in atmospheres predominated by molecular H is not as rapid as predicted by elastic collision theory; (3) the net quantum loss of NH3 in the presence of a 200 fold excess of H2 is 0.02, much higher than was expected from the amount of H2 present; (4) the conversion of NH3 into N2 in plasmas associated with infalling meteors is very efficient and rapid, and could account for most of the N2 present on Titan; (5) the yields of C2H2 and HCN from lightning induced chemistry in mixtures of CH4 and N2 is consistent with quenched thermodynamic models of the discharge core; and (6) photolysis induced by the UV light emitted by the gases in the hot plasmas may account for some, if not most, of the excess production of C2H6 and the more complex hydrocarbons.

Scattergood, Thomas W.↗

High Efficiency Thermoelectric Radioisotope Power Systems

The work performed and whose results presented in this report is a joint effort between the University of New Mexico s Institute for Space and Nuclear Power Studies (ISNPS) and the Jet Propulsion Laboratory (JPL), California Institute of Technology. In addition to the development, design, and fabrication of skutterudites and skutterudites-based segmented unicouples this effort included conducting performance tests of these unicouples for hundreds of hours to verify theoretical predictions of the conversion efficiency. The performance predictions of these unicouples are obtained using 1-D and 3-D models developed for that purpose and for estimating the actual performance and side heat losses in the tests conducted at ISNPS. In addition to the performance tests, the development of the 1-D and 3-D models and the development of Advanced Radioisotope Power systems for Beginning-Of-Life (BOM) power of 108 We are carried out at ISNPS. The materials synthesis and fabrication of the unicouples are carried out at JPL. The research conducted at ISNPS is documented in chapters 2-5 and that conducted at JP, in documented in chapter 5. An important consideration in the design and optimization of segmented thermoelectric unicouples (STUs) is determining the relative lengths, cross-section areas, and the interfacial temperatures of the segments of the different materials in the n- and p-legs. These variables are determined using a genetic algorithm (GA) in conjunction with one-dimensional analytical model of STUs that is developed in chapter 2. Results indicated that when optimized for maximum conversion efficiency, the interfacial temperatures between various segments in a STU are close to those at the intersections of the Figure-Of-Merit (FOM), ZT, curves of the thermoelectric materials of the adjacent segments. When optimizing the STUs for maximum electrical power density, however, the interfacial temperatures are different from those at the intersections of the ZT curves, but close to those at the intersections the characteristic power, CP, curves of the thermoelectric materials of the adjacent segments (CP = T(sup 2)Zk and has a unit of W/m). Results also showed that the number of the segments in the n- and p-legs of the STUs optimized for maximum power density are generally fewer than when the same unicouples are optimized for maximum efficiency. These results are obtained using the 1-D optimization model of STUs that is detailed in chapter 2. A three-dimensional model of STUs is developed and incorporated into the ANSYS commercial software (chapter 3). The governing equations are solved, subject to the prescribed

El-Genk, Mohamed↗

Gasification and Techno-Economic Analysis of High-Impact Biomass Feedstocks for the Synthesis of High-Octane Gasoline

Five economically advantaged biomass feedstocks identified in the US Department of Energy's 2016 Billion Ton Study were gasified and the syngas reformed at the bench scale to study the feedstock price-performance relationship. The distribution of reformed syngas compositions, heating values, and yields were similar across the different feedstocks and blends thereof, which ranged from inexpensive residual wastes to more expensive and higher quality biomass, revealing that feedstock performance was mostly insensitive to its price. Custom blended feedstocks produced syngas with characteristics resembling linear combinations of syngas from single-component feedstocks, supporting the ability to customize and predict blended properties based on single-feedstock data. A techno-economic analysis of specific feedstock costs for producing high-octane gasoline showed that miscanthus and forest residues were the most cost-effective with gas yields consistent with experimental gasification data. A field-to-wheels life-cycle assessment of greenhouse gas emissions showed that forest residues was the most environmentally benign feedstock of those studied.

09 BIOMASS FUELS↗

Biosynthesis of polycyclopropanated high energy biofuels

Cyclopropane-functionalized hydrocarbons are excellent fuels due their high energy density. However, the organic synthesis of these molecules is challenging and harmful to the environment. In this work we produced polycyclopropanated fatty acids in bacteria. These molecules can be easily converted into renewable fuels for high energy applications such as shipping, long-haul transport, aviation, and rocketry. We explored the chemical diversity encoded in the genome of thousands of bacteria to identify and repurpose naturally occurring cyclopropanated molecules. We identified a set of candidate iterative polyketide synthases (iPKSs) predicted to produce polycyclopropanated fatty acids (POP-FAs), expressed these PKSs in Streptomyces coelicolor and produced the POP-FAs. We determined the structure of the molecules and increased their production 22-fold. Polycyclopropanated fatty acid methyl esters (POP-FAMEs) were obtained by methyl esterifying the POP-FAs. Finally, we calculated that our POP fuel candidates can have energy densities of more than 50 MJ/L. Our research shows that POP-FAMEs and other polyketide derived POPs have the energetic properties for energy-demanding applications for which sustainable alternatives are scarce.

09 BIOMASS FUELS↗

Asymmetrical C–C Coupling for Electroreduction of CO on Bimetallic Cu–Pd Catalysts

Electroreduction of carbon monoxide (CO) possesses great potential for achieving the renewable synthesis of hydrocarbon chemicals from CO 2 . We report here selective reduction of CO to acetate using Cu–Pd bimetallic electrocatalysts. High activity and selectivity are demonstrated for CO-to-acetate conversion with >200 mA/cm 2 in geometric current density and >65% in Faradaic efficiency (FE). An asymmetrical C–C coupling mechanism is proposed to explain the composition-dependent catalytic performance and high selectivity toward acetate. This mechanism is supported by the computationally predicted shift of the *CO adsorption from the top-site configuration on Cu (or Cu-rich) surfaces to the bridge sites of Cu–Pd bimetallic surfaces, which is also associated with the reduction of the CO hydrogenation barrier. Further kinetic analysis of the reaction order with respect to CO and Tafel slope supports a reaction pathway with *CO–*CHO recombination following a CO hydrogenation step, which could account for the electroreduction of CO to acetate on the Cu–Pd bimetallic catalysts. Furthermore, our work highlights how heteroatomic alloy surfaces can be tailored to enable distinct reaction pathways and achieve advanced catalytic performance beyond monometallic catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Statistical Multiobjective Optimization of Thiospinel CoNi 2 S 4 Nanocrystal Synthesis via Design of Experiments

Thiospinels, such as CoNi 2 S 4 , are showing promise for numerous applications, including as catalysts for the hydrogen evolution reaction, hydrodesulfurization, and oxygen evolution and reduction reactions; however, CoNi 2 S 4 has not been synthesized as small, colloidal nanocrystals with high surface-area-to-volume ratios. Traditional optimization methods to control nanocrystal attributes such as size typically rely upon one variable at a time (OVAT) methods that are not only time and labor intensive but also lack the ability to identify higher-order interactions between experimental variables that affect target outcomes. Herein, we demonstrate that a statistical design of experiments (DoE) approach can optimize the synthesis of CoNi 2 S 4 nanocrystals, allowing for control over the responses of nanocrystal size, size distribution, and isolated yield. After implementing a 2 5–2 fractional factorial design, the statistical screening of five different experimental variables identified temperature, Co:Ni precursor ratio, Co:thiol ratio, and their higher-order interactions as the most critical factors in influencing the aforementioned responses. Second-order design with a Doehlert matrix yielded polynomial functions used to predict the reaction parameters needed to individually optimize all three responses. A multiobjective optimization, allowing for the simultaneous optimization of size, size distribution, and isolated yield, predicted the synthetic conditions needed to achieve a minimum nanocrystal size of 6.1 nm, a minimum polydispersity (σ/$\bar{d}$) of 10%, and a maximum isolated yield of 99%, with a desirability of 96%. The resulting model was experimentally verified by performing reactions under the specified conditions. Furthermore, our work illustrates the advantage of multivariate experimental design as a powerful tool for accelerating control and optimization in nanocrystal syntheses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Lattice disorder effect on magnetic ordering of iron arsenides

This study investigates magnetic ordering temperature in nano- and mesoscale structural features in an iron arsenide. Although magnetic ground states in quantum materials can be theoretically predicted from known crystal structures and chemical compositions, the ordering temperature is harder to pinpoint due to potential local lattice variations that calculations may not account for. In this work we find surprisingly that a locally disordered material can exhibit a significantly larger Néel temperature (T N ) than an ordered material of precisely the same chemical stoichiometry. Here, a EuFe 2 As 2 crystal, which is a ‘122’ parent of iron arsenide superconductors, is found through synthesis to have ordering below T N = 195 K (for the locally disordered crystal) or T N = 175 K (for the ordered crystal). In the higher T N crystals, there are shorter planar Fe-Fe bonds [2.7692(2) Å vs. 2.7745(3) Å], a randomized in-plane defect structure, and diffuse scattering along the [00 L] crystallographic direction that manifests as a rather broad specific heat peak. For the lower T N crystals, the a-lattice parameter is larger and the in-plane microscopic structure shows defect ordering along the antiphase boundaries, giving a larger T N and a higher superconducting temperature (T c ) upon the application of pressure. First-principles calculations find a strong interaction between c-axis strain and interlayer magnetic coupling, but little impact of planar strain on the magnetic order. Neutron single-crystal diffraction shows that the low-temperature magnetic phase transition due to localized Eu moments is not lattice or disorder sensitive, unlike the higher-temperature Fe sublattice ordering. This study demonstrates a higher magnetic ordering point arising from local disorder in 122.

36 MATERIALS SCIENCE↗

Heterologous expression of a fully active Azotobacter vinelandii nitrogenase Fe protein in Escherichia coli

ABSTRACT The functional versatility of the Fe protein, the reductase component of nitrogenase, makes it an appealing target for heterologous expression, which could facilitate future biotechnological adaptations of nitrogenase-based production of valuable chemical commodities. Yet, the heterologous synthesis of a fully active Fe protein of Azotobacter vinelandii ( Av NifH) in Escherichia coli has proven to be a challenging task. Here, we report the successful synthesis of a fully active Av NifH protein upon co-expression of this protein with Av IscS/U and Av NifM in E. coli . Our metal, activity, electron paramagnetic resonance, and X-ray absorption spectroscopy/extended X-ray absorption fine structure (EXAFS) data demonstrate that the heterologously expressed Av NifH protein has a high [Fe 4 S 4 ] cluster content and is fully functional in nitrogenase catalysis and assembly. Moreover, our phylogenetic analyses and structural predictions suggest that Av NifM could serve as a chaperone and assist the maturation of a cluster-replete Av NifH protein. Given the crucial importance of the Fe protein for the functionality of nitrogenase, this work establishes an effective framework for developing a heterologous expression system of the complete, two-component nitrogenase system; additionally, it provides a useful tool for further exploring the intricate biosynthetic mechanism of this structurally unique and functionally important metalloenzyme. IMPORTANCE The heterologous expression of a fully active Azotobacter vinelandii Fe protein (AvNifH) has never been accomplished. Given the functional importance of this protein in nitrogenase catalysis and assembly, the successful expression of AvNifH in Escherichia coli as reported herein supplies a key element for the further development of heterologous expression systems that explore the catalytic versatility of the Fe protein, either on its own or as a key component of nitrogenase, for nitrogenase-based biotechnological applications in the future. Moreover, the “clean” genetic background of the heterologous expression host allows for an unambiguous assessment of the effect of certain nif-encoded protein factors, such as AvNifM described in this work, in the maturation of AvNifH, highlighting the utility of this heterologous expression system in further advancing our understanding of the complex biosynthetic mechanism of nitrogenase.

59 BASIC BIOLOGICAL SCIENCES↗

Surface-mediated spontaneous emulsification of the acylated peptide, semaglutide

Acylated peptides composed of glucagon-like peptide-1 receptor agonists modified with a fatty acid side chain are an important class of therapeutics for type 2 diabetes and obesity but are susceptible to an unusual physical instability in the presence of hydrophobic surfaces, i.e., spontaneous emulsification, also known as ouzo formation in practice. In this work, light scattering, small-angle X-ray scattering, and circular dichroism measurements are used to characterize the physical properties of the semaglutide colloidal phase, including size distribution, shape, secondary structure, internal structure, and internal composition, as a function of solution physico-chemical conditions. The existence and size of the colloids formed are successfully predicted by a classical Rayleigh model, which identifies the parameters controlling their size and formation. Colloid formation is found to be catalyzed by hydrophobic surfaces, and formation rates are modeled as an autocatalytic reaction, enabling the formation of a master curve for various surfaces that elucidates the mechanism. Surfaces differ due to differences in surface wettability, which can be correlated with Hansen solubility parameters. This work provides insights into this unusual colloidal phenomenon and guides the peptide synthesis process and drug product formulation in the pharmaceutical industry.

59 BASIC BIOLOGICAL SCIENCES↗

Artificial Intelligence Transforming Post-Translational Modification Research

Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗