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DOE OSTI2024

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties

The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic–lipophilic difference equation used to predict microemulsions’ phase behavior expresses the oils’ physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. As a result, the best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R² score of 0.9

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Visualizing the Three-Dimensional Arrangement of Hydrogen Atoms in Organic Molecules by Coulomb Explosion Imaging

Structure-sensitive methods based on femtosecond light or electron pulses are now making it possible to measure how molecular structures change during light-induced processes. Despite significant progress, high-fidelity imaging of nuclear positions remains a challenge even for relatively small molecular systems and, notably, regarding the positions of hydrogen atoms. As demonstrated in recent work, X-ray-induced Coulomb explosion imaging (CEI) may overcome this obstacle, as its sensitivity does not depend on the mass of the imaged atoms. The photoinduced ring opening of the heterocyclic molecule 2(5 H )-thiophenone has attracted recent interest. Here, in this work, we show that CEI offers a powerful route to imaging the peripheral H atoms in this molecule and thus, more generally, to tracking detailed nuclear motions (e.g., isomerizations) in organic molecules on ultrafast time scales. Specifically, we record momentum-space Coulomb explosion images that report on the three-dimensional positioning of all nuclei within the molecule, for instance, distinguishing H atoms in C–H bonds that lie within or are directed out of the plane defined by the heavy atoms. The prospect of imaging peripheral H atoms to probe photochemical dynamics is explored by coupling ab initio molecular dynamics with classical Coulomb explosion simulations, thereby differentiating potential photoproduct isomers, including those whose structures primarily differ in the position of the hydrogens.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Synthetically Reversible, Proton-Mediated Nitrite N–O Bond Cleavage at a Dicopper Site

A monocationic dicopper(I,I) nitrite complex [Cu 2 (μ-κ 1 :κ 1 -O 2 N)DPFN][NTf 2 ] (2) (DPFN = 2,7-bis(fluoro-di(2-pyridyl)methyl)-1,8-naphthyridine, NTf 2 – = N(SO 2 CF 3 ) 2 – ), was synthesized by treatment of a dicopper acetonitrile complex, [Cu 2 (μ-MeCN)DPFN][NTf 2 ] 2 (1), with tetrabutylammonium nitrite ([nBu 4 N][NO 2 ]). DFT calculations indicate that 2 is one of three linkage isomers that are close in energy and presumably accessible in solution. Reaction of the μ-κ 1 :κ 1 -O 2 N complex with p-TolSH produces nitrous acid (HONO) and the corresponding dicopper thiolate species via an acid–base exchange reaction. Notably, treatment of 2 with HNTf 2 results in N–O bond cleavage in the putative, HONO-ligated complex to form the more thermodynamically favorable nitrosyl-bridged dicopper complex [Cu 2 (μ-NO)(μ-OH)DPFN][NTf 2 ] 2 (4). This scission can be reversed via deprotonation of the hydroxy ligand with KO t Bu. X-ray diffraction studies confirmed the solid-state molecular structures of 2 and 4. DFT calculations were used to construct a reaction coordinate diagram detailing formation of the μ-NO complex and to describe its electronic structure. The nitrosyl ligand in 4 is chemically labile, as demonstrated by its ready displacement in reactions with CO or NO 2 – .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Engineering Enantiocomplementary Protoglobins for Stereoconvergent Construction of N -Alkylated α-Aminoketones

The synthesis of enantiopure compounds from a mixture of E/Z alkenes represents a notable challenge in synthetic chemistry. While enzymes excel in achieving unparalleled selectivity, their inherent specificity often confines activity to a single stereoisomeric substrate, consequently restricting the overall efficiency of such transformations. Here, we demonstrate that protoglobin-derived hemoproteins can catalyze stereoconvergent intermolecular amination using simple N-alkyl hydroxylamines as nitrene precursors, a transformation which remains elusive in synthetic chemistry. These engineered enzymes process E/Z mixtures of silyl enol ethers, enabling the precise incorporation of N-alkyl amino moieties (−NHAlkyl) into diverse molecular structures (up to 79% yield and 95% ee). Two complementary protoglobin variants were engineered using directed evolution to enable enantiodivergent synthesis of both enantiomers of α-aminoketones. This enzymatic platform achieves stereoconvergent and enantiodivergent transformations, facilitating the conversion of simple chemicals into an array of valuable pharmaceutical compounds featuring aminoketone functionalities.

Alcohols↗

DOE OSTI2024

Characterize and control the multi-range structure of solution-phase systems with resonant x-ray scattering

This work aims at demonstrating the feasibility and impact of Anomalous X-ray Scattering (AXS) in characterizing the multi-range structures of solution-state systems. We will show preliminary investigation of long-range correlations in concentrated aqueous electrolytes with a combination of AXS and molecular dynamics (MD) simulations. We will also start exploring the capability of AXS to capture intra- and inter-molecular structure of dilute molecular systems, and specifically its sensitivity to the chemical environment surrounding active metal sites. We anticipate that the development of this method will help us to understand ion solvation and transport, which affect the performances of, for instance, electrocatalytic cells, as well as to identify/control the intrinsic structural factors that lead to selectivity, efficiency, and stability control during catalysis.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Fostering a Guiding Multiscale Model for the Development of Advanced MgB 2 Hydrogen Storage Materials (Final Technical Report)

Project Goal and Objective. The demand for energy and for an upgraded energy infrastructure has steadily grown, as have the needs for energy independence and alternatives to our reliance on petroleum. Hydrogen is considered the most viable fuels for wide-scale implementation in the near future as it is less-polluting, non-toxic, and has more stored energy than petroleum. It is envisioned that hydrogen can eventually become the prime energy carrier, integrating the transportation, grid, and chemical sectors in a way that improves resiliency, diversifies feedstocks, and affords new economic opportunities. A key remaining challenge is the development materials with enhanced gravimetric and volumetric hydrogen storage capacities that offer a higher performance than compressed gas. These materials would eliminate the need for large-scale compression, thereby dramatically reducing the footprint and cost of gas storage. The high gravimetric and volumetric hydrogen capacities of complex hydrides has prompted an intensive investigation of the potential of this class of materials as hydrogen storage media over the past 25 years. Among the many complex hydrides that have been explored, magnesium borohydride, Mg(BH 4 ) 2 , has been found to possess the best combination of practical thermodynamic properties. These include a gravimetric H 2 density of 14.9 wt% H 2 and thermodynamics for the dehydrogenation of Mg(BH 4 ) 2 to MgB 2 (equation 1) (ΔH° = 39 kJ/mol H 2 , ΔS = 112 J/K mol H 2 ) which lie in the narrow window required Mg(BH 4 ) 2 $\Leftrightarrow$ MgB 2 + 4 H 2 (1) for reversibility under moderate pressure and temperature. However, overcoming the extremely slow kinetics of the reversible release of hydrogen by this material in the solid state is a daunting challenge. At temperatures greater than 400 °C, the borohydride releases up to 14 wt% hydrogen giving MgB 2 . We discovered that the direct re-hydrogenation of MgB 2 to Mg(BH 4 ) 2 can be accomplished under 950 bar H 2 at 400 °C. While this demonstrated that complete reversibility can be achieved, the conditions employed are far too extreme for commercial hydrogen storage applications. More recently, we found through US DOE funded research projects (EERE HyMARC and HySCOR), that hydrogen cycling, can be accomplish at much milder conditions upon modification of the borohydride or boride. Guided by these discoveries these discoveries, the objective of this research project was to obtain key information that will enable the development of a model of reversible hydrogenation of MgB 2 to Mg(BH 4 ) 2 . The ultimate goal of our efforts is to attain a model of this transformation that can be utilized to accelerate development further advanced materials. This project directly follows on discoveries that were made over the course of a US DOE, EERE HyMARC project that was focused on improvement of the hydrogen cycling kinetics of modified MgB 2 . We found that that mechanical milling with graphene results the desired, pronounced kinetic enhancement. The dramatic lowering of the conditions required for the hydrogenation of MgB 2 is a significant step towards overcoming its chemical inertness allowing its development as a practical onboard hydrogen storage material. However, the exact nature of the modification(s) of MgB 2 that is responsible for its activation towards hydrogenation is completely unknown. This situation is not unique, as efforts to develop hydrogen storage materials typically have a narrow focus rather than a comprehensive approach that takes atomic level bonding and structure; molecular dynamics; long range, nano- and mesoscale-structure and their interconnection all into account. The goal of this project was the development of a comprehensive, multi-scale computational model of reversible hydrogenation of MgB 2 to Mg(BH 4 ) 2 that can be utilized for development of higher performance versions of the modified material. Development of the model requires determination of: 1) the bulk, nano-scale, and meso-scale structural changes occurring at elevated pressure following mechano-chemical modification of MgB 2 ; 2) the reaction pathway of the reversible hydrogenation of MgB 2 to Mg(BH 4 ) 2 ; 3) the effect of elevated pressure and mechano-chemical modification on the chemical reaction pathways; 4) the interactions at solid-gas interfaces; and particle surfaces; and 5) the kinetics and thermodynamic parameters associated with each step of the hydrogenation reaction pathway. This investigation required advanced techniques as preliminary, standard XRD, 11 B NMR, and FTIR analysis showed no signs of material modification. In order to gain this level of understanding of modified MgB 2 , required the teaming of a diverse group of experts and state-of-the art experimental capabilities at the University of Hawaii at Manoa (UHM) and collaborating National Laboratories: Craig Jensen , Department of Chemistry (PI and Project Director), solid state, solution, and high pressure NMR spectroscopy; solid-state synthesis; and high pressure hydrogenation (collaboration with SNL); Godwin Severa , Hawaii Natural Energy Institute (co-PI) calorimetry; infrared and Raman spectroscopy (collaboration with NREL); Dera , high pressure X-ray diffraction including in situ experiments (collaboration with ANL); Hope Ishii , Hawaii Institute of Geophysics electron microscopy investigations (collaboration with LBNL); and Joe Brown , Mechanical Engineering , material electronic structure and electric field effects.

08 HYDROGEN↗

DOE OSTI2025

Open-source generation of sigma profiles: impact of quantum chemistry and solvation treatment on machine learning performance

The combination of machine learning (ML) models with chemistry-related tasks requires the description of molecular structures in a machine-readable way. The nature of these so-called molecular descriptors has a direct and major impact on the performance of ML models and remains an open problem in the field. Structural descriptors like SMILES strings or molecular graphs lack size-independence and can be memory intensive. Machine-learned descriptors can be of low dimensionality and constant size but lack physical significance and human interpretability. Sigma profiles, which are unnormalized histograms of the surface charge distributions of solvated molecules, combine physical significance with low dimensionality and size-independence, making them a suitable candidate for a universal molecular descriptor. However, their widespread adoption in ML applications requires open access to sigma profile generation, which is currently not available. This work details the development of OpenSPGen – an open-source tool for generating sigma profiles. Also presented are studies on the effect of different settings on the efficacy of the generated sigma profiles at predicting thermophysical material properties when used as inputs to a Gaussian process as a simple surrogate ML model. We find that a higher level of theory does not translate to more accurate results. We also provide further recommendations for sigma profile calculation and use in ML models.

Salih, Fathya Y. M. [University of Notre Dame, IN ↗

DOE OSTI2026

Electron–Ion Covariance Reveals Geometry-Resolved Inner-Shell Spectra in CS 2 Photodissociation

The chemical shifts of inner-shell atomic orbitals are highly sensitive to chemical bonding and molecular structure. In evolving systems, however, the spectra of distinct molecular species and geometries overlap, obscuring the underlying chemical dynamics. Here we demonstrate the use of electron–ion covariance analysis to combine the structural sensitivity of Coulomb explosion imaging with inner-shell spectroscopy, yielding geometry-specific spectra of transient and product species. We apply this approach to the excited state dynamics of CS 2 probed by ionization above the S 2p edge. Electron–ion covariance with time- and momentum-selected S + and S 2+ ions isolates distinct S 2p photoelectron spectra for ground-state CS 2 , bent photoexcited CS 2 , the CS photoproduct, and bare atomic sulfur ─ species whose spectra overlap strongly in the channel-averaged measurement. Clear chemical shifts are observed in the covariance photoelectron spectrum for each of these species, all of which are consistent with high-level calculations. Here, by extracting the atomic S contribution to the photoelectron spectrum in a finely time-resolved manner we can disentangle this contribution to the overall time-resolved photoelectron spectrum as the photodissociation proceeds. These results demonstrate the promise of electron–ion covariance as a general approach to geometry-resolved inner-shell spectroscopy, opening a route to tracking structural evolution through chemical shifts in complex photoexcited molecules.

Ionization↗

DOE OSTI2025

Electronic Structure Distortions in Chromium Chelates Impair Redox Kinetics in Flow Batteries

Aminopolycarboxylate chelates are emerging as a promising class of electrolyte materials for aqueous redox flow batteries, offering tunable redox potentials, solubility, and pH stability through careful selection of ligands and transition metal ions. Despite their potential, the impact of molecular structure modifications on the electronic and electrochemical properties of these chelates remains underexplored. Here, in this study, we examine how introducing a hydroxyl group, often employed for its solubilizing properties, to the backbone of CrPDTA, a reference chelate material, significantly changes the thermodynamics and kinetics of the chelate's redox process. We correlate changes in molecular and electronic structures to different electrochemical responses resulting from the hydroxyl addition and show that the introduction of this functional group leads to a distortion in the octahedral coordination of chromium. Furthermore, increased anisotropic spin density and nonintegral oxidation state changes in the Cr metal center result in a larger barrier for electron transfer in CrPDTA‐OH. It is demonstrated that preserving a hexacoordinate chelate structure across a broad pH range is crucial for efficient flow battery application and it is emphasized that ligand modifications must avoid distorting the octahedral coordination of the transition metal.

25 ENERGY STORAGE↗

DOE OSTI2024

Evaluating the Use of Foundational Chemical Language Models in Multimodal Graph Fusion

Rapid and accurate prediction of the physicochemical properties of molecules given their structures remains a key challenge in cheminformatics. Machine learning approaches offer high-throughput options, but the optimality of inductive biases and data representations are up for debate. For example, BERT-based masked language models (MLMs) can be trained in a self-supervised way on hundreds of millions to billions of readily available SMILES strings. Another option is graph neural networks (GNNs), which can operate directly on molecular structures. Yet, generating accurate molecular geometry is computationally expensive, leading to a relative scarcity in data compared to SMILES strings. It is attractive to combine these two paradigms by pre-training an LM on a large corpus of SMILES strings and embedding these representation into a geometric graph neural network. Despite the promise of such an approach, and contrary to previous studies, we find mixed results with the combination of the LMs and GNNs on several molecule datasets. In particular, we found evidence for improvement on the FreeSolv and QM7 benchmarks, but degraded performance on the ESOL, LIPO and QM9 datasets compared to a GNN baseline.

Francel, Collin [University of Alabama]↗

DOE OSTI2025

Acetylcholinesterase: Structure, dynamics, and interactions with organophosphorus compounds

Acetylcholinesterase (AChE) is an enzyme that hydrolyzes the neurotransmitter acetylcholine (ACh), removing it from the synaptic cleft after the transmission of an electrical signal, making it an essential component of chemical neurotransmission. AChE is a serine hydrolase, containing a catalytic triad of Ser/His/Glu. AChE is a prime target for pharmaceuticals treating a variety of neurological disorders. It is also the target of synthetic organophosphorus (OP) compounds that have been used as pesticides and chemical warfare agents. OP compounds contain a potent leaving group, such as fluorine, and act by forming a covalent adduct with the catalytic serine of the AChE active site. A wealth of structural information is available for AChE, including over 300 structures, including a subset of structures in complex with drugs as well as OP compounds. This review will highlight the interactions between OP compounds and AChE from a structural and computational perspective, with a discussion of access to the active site, as well as side reactions that lead to dealkylation of the OP-catalytic serine adduct, a process known as aging. We conclude that while the majority of the conformational changes needed to accommodate the OP compounds are localized to the acyl loop in the crystal structures, molecular dynamics simulations highlight the potential for a far more dynamic enzyme.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Integrated machine learning-molecular dynamics framework for electrolyte property prediction

Electrochemical stability windows determine the operating range of battery electrolytes, yet accurate prediction remains challenging because stability emerges from statistical ensembles of local solvation environments rather than single ground-state molecular structures. Traditional density functional theory calculations on energy-minimized clusters cannot capture the thermal variations in local coordination environments and geometries that govern decomposition, while SMILES-based machine learning methods lack explicit representation of three-dimensional solvation structure and ion pairing. Here, we introduce a structure-aware machine learning framework that predicts frontier orbital energies (HOMO and LUMO) directly from molecular dynamics-sampled solvation configurations, achieving sub-0.6 eV accuracy at computational costs 3–4 orders of magnitude lower than first-principles methods. Across twelve representative battery electrolytes, we demonstrate that solvent-separated and contact ion pairs exhibit strong size- and local chemistry dependent electronic stability, with variations in coordination shifts of HOMO or LUMO level by 2–3 eV, and that extended solvation structure and partially desolvated environment further modulate stability by up to 3 eV. By encoding the statistical nature of electrochemical failure through ensemble sampling of explicit solvation geometries, our approach enables high-throughput screening and rational design of next-generation battery electrolytes with mechanistic understanding of structure–property relationships.

Energy - Storage↗

DOE OSTI2025

Design principles of spacer cations for suppressing phase segregation in 2D halide perovskites

Suppression of photoinduced halide segregation in mixed halide perovskites remains a significant challenge for their application as wide bandgap semiconductors in solar cells. In addition to stability issues, halide segregation leads to a loss in power conversion efficiency in solar cells and a shift in emission wavelength in light-emitting devices. However, employing low-dimensional halide perovskites, such as two-dimensional (2D) or quasi-2D structures, offers a strategy to mitigate this segregation. Here, we have systematically studied how the molecular structure and binding configuration of spacer cations, ranging from linear alkyl chains to aromatic structures, affect photoinduced halide segregation across both Ruddlesden–Popper (RP) and Dion–Jacobson (DJ) frameworks in 2D mixed halide perovskites (Br : I = 50 : 50). Aromatic spacer cations within the DJ perovskite configuration were found to suppress segregation most effectively. For example, the halide segregation rate in a 2D mixed halide perovskite film with the DJ phase using the aromatic spacer cation 1,4-phenylenedimethanammonium (PDMA) was 9.3 × 10 −4 s −1 —an order of magnitude lower than that observed with linear 2D RP perovskites employing butylammonium (BA) as the spacer cation (6.1 × 10 −3 s −1 ). Spectroscopic studies detailing the influence of spacer cation selection in mixed halide perovskites for suppressing phase segregation are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Composition-Nanoarchitecture-Performance Analysis of High Energy Density Electrodeposited Silicon for Lithium-Ion Battery Anodes

Electrodeposition of silicon (Si) was previously demonstrated as a promising method for fabricating 3D-structured lithium-ion battery anodes. However, the relationship between the electrochemical performance and chemical composition of the relatively impure electrodeposited silicon is not well understood. Here, we report the electrodeposition of a Si-dominant active material (EDEP-Si) onto 3D-structured nickel (Ni) scaffolds and systematically compare the electrochemical properties, elemental composition, atomistic Si coordination, and molecular structure of EDEP-Si with high-purity amorphous Si grown via static chemical vapor deposition. Despite the considerable amount of carbon (9–11 at %) and oxygen (42–44 at %) present in EDEP-Si, the cycling stability and high reversible specific capacity are remarkably similar to those of CVD-Si on a silicon basis (~2400 mA h/g-Si after 100 cycles). The primary difference is that EDEP-Si exhibits reduced cycling efficiency over the first 10–20 cycles. Reactions between carbon and, more importantly, oxygen in EDEP-Si with lithium are likely responsible for the reduced early cycle performance and lower capacity of the total deposit. Finally, our observations suggest ultrapure Si is not necessary for high electrochemical access to reversible charge storage, although limiting the presence of incorporated impurity species would improve energy density and first cycle efficiency.

25 ENERGY STORAGE↗

DOE OSTI2025

Generalizable, fast, and accurate DeepQSPR with fastprop

Abstract Quantitative Structure–Property Relationship studies (QSPR), often referred to interchangeably as QSAR, seek to establish a mapping between molecular structure and an arbitrary target property. Historically this was done on a target-by-target basis with new descriptors being devised to specifically map to a given target. Today software packages exist that calculate thousands of these descriptors, enabling general modeling typically with classical and machine learning methods. Also present today are learned representation methods in which deep learning models generate a target-specific representation during training. The former requires less training data and offers improved speed and interpretability while the latter offers excellent generality, while the intersection of the two remains under-explored. This paper introduces , a software package and general Deep-QSPR framework that combines a cogent set of molecular descriptors with deep learning to achieve state-of-the-art performance on datasets ranging from tens to tens of thousands of molecules. provides both a user-friendly Command Line Interface and highly interoperable set of Python modules for the training and deployment of feedforward neural networks for property prediction. This approach yields improvements in speed and interpretability over existing methods while statistically equaling or exceeding their performance across most of the tested benchmarks. is designed with Research Software Engineering best practices and is free and open source, hosted at github.com/jacksonburns/fastprop.

Burns, Jackson W. (ORCID:0000000206579426)↗

DOE OSTI2025

Structural and functional analyses of SARS-CoV-2 Nsp3 and its specific interactions with the 5’ UTR of the viral genome

ABSTRACT Non-structural protein 3 (Nsp3) is the largest open reading frame encoded in the SARS-CoV-2 genome, essential for the formation of double-membrane vesicles (DMV) wherein viral RNA replication occurs. We conducted an extensive structure-function analysis of Nsp3 and determined the crystal structures of the ubiquitin-like 1 (Ubl1), nucleic acid binding (NAB), β-coronavirus-specific marker (βSM) domains, and a sub-region of the Y domain of this protein. We show that the Ubl1, ADP-ribose phosphatase (ADRP), human SARS Unique (HSUD), NAB, and Y domains of Nsp3 bind the 5’ UTR of the viral genome and that the Ubl1 and Y domains possess affinity for recognition of this region, suggesting high specificity. The Ubl1-Nucleocapsid (N) protein complex binds the 5’ UTR with greater affinity than the individual proteins alone. Our results suggest that multiple domains of Nsp3, particularly Ubl1 and Y, shepherd the 5’ UTR of the viral genome during translocation through the DMV membrane, priming the Ubl1 domain to load the genome onto N protein. IMPORTANCE The largest protein encoded by the SARS-CoV-2 genome is Nsp3. In infected cells, this multi-domain protein forms a pore structure in the virus-induced double-membrane vesicles (DMV). We have incomplete data on Nsp3 molecular structure, and here, we describe crystal structures for multiple domains of Nsp3. It is thought that newly replicated viral RNA transits through the DMV pore; however, we possess incomplete data on which regions of Nsp3 actually interact with RNA. Here, we present data showing that five domains of Nsp3 interact with the 5’ UTR of the SARS-CoV-2 RNA, including the Y domain for which no function has ever been discovered. These data suggest that the pore structure plays an active role in recognizing the terminal end of the genome, transiting and loading the viral RNA onto the cytoplasmic nucleocapsid protein. These data help expand our knowledge of Nsp3 structure and function and the SARS-CoV-2 replication cycle.

Microbiology↗

DOE OSTI2024

Small‐Molecule Mixed Ionic‐Electronic Conductors for Efficient N‐Type Electrochemical Transistors: Structure‐Function Correlations

Abstract The fundamental challenge in electron‐transporting organic mixed ionic‐electronic conductors (OMIECs) is simultaneous optimization of electron and ion transport. Beginning from Y6‐type/U‐shaped non‐fullerene solar cell acceptors, we systematically synthesize and characterize molecular structures that address the aforementioned challenge, progressively introducing increasing numbers of oligoethyleneglycol (OEG; g) sidechains from 1 g to 3 g, affording OMIECs 1gY, 2gY, and 3gY, respectively. The crystal structure of 1gY preserves key structural features of the Y n series: a U‐shaped/planar core, close π–π molecular stacking, and interlocked acceptor groups. Versus inactive Y6 and Y11, all of the new glycolated compounds exhibit mixed ion‐electron transport in both conventional organic electrochemical transistor (cOECT) and vertical OECT (vOECT) architectures. Notably, 3gY with the highest OEG density achieves a high transconductance of 16.5 mS, an on/off current ratio of ~10 6 , and a turn‐on/off response time of 94.7/5.7 ms in vOECTs. Systematic optoelectronic, electrochemical, architectural, and crystallographic analysis explains the superior 3gY‐based OECT performance in terms of denser n gY OEG content, increased crystallite dimensions with decreased long‐range crystalline order, and enhanced film hydrophilicity which facilitates ion transport and efficient redox processes. Finally, we demonstrate an efficient small‐molecule‐based complementary inverter using 3gY vOECTs, showcasing the bioelectronic applicability of these new small‐molecule OMIECs.

Cho, Yongjoon↗

DOE OSTI2024

Small‐Molecule Mixed Ionic‐Electronic Conductors for Efficient N‐Type Electrochemical Transistors: Structure‐Function Correlations

Cho, Yongjoon↗