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All sources NASA NTRS DOE OSTI

DOE OSTI2024

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↗

DOE OSTI2026

Simultaneous Overexpression of FERULOYL‐CoA 6′‐HYDROXYLASE 1 and COUMARIN SYNTHASE Leads to Coumarin‐Enriched Lignin and Improved Saccharification in Greenhouse‐ and Field‐Grown Poplar

ABSTRACT The urgent need for renewable resources has increased the interest in woody biomass to manufacture bio‐based products. However, lignin recalcitrance limits the enzymatic conversion of wood into fermentable sugars, posing a major challenge for biomass deconstruction. To address this problem, we aimed at incorporating the coumarin scopoletin into the lignin polymer of poplar ( Populus tremula × P . alba ) by expressing FERULOYL‐CoA 6′‐HYDROXYLASE 1 ( F6′H1 ) and COUMARIN SYNTHASE ( COSY ) in lignifying cells. Three constructs were evaluated: two bicistronic constructs, SCOP1 ( COSY followed by F6′H1 ) and SCOP2 ( F6′H1 followed by COSY ), and one monocistronic, SCOP3 (only F6′H1 ). SCOP1 poplars produced most free scopoletin without altering overall lignin, cellulose or hemicellulose content. SCOP2 poplars were overall less efficient in scopoletin production and most of these lines showed a severe biomass yield penalty, whereas SCOP3 caused plant lethality. NMR and metabolic analyses confirmed that scopoletin cross‐coupled with G and S monomers during lignification in SCOP1 lines. In addition to scopoletin, the detection of benzodioxane structures revealed the incorporation of dihydroxycoumarins. Overall coumarin incorporation in lignin amounted up to 2.3%. After alkaline pretreatment, wood from greenhouse‐grown SCOP1 poplars released up to 29% more glucose compared to the wild type upon limited saccharification. Field‐testing of three SCOP1 lines showed a 6 to 11% increase in saccharification efficiency, with the line containing the lowest scopoletin levels maintaining normal growth. These results demonstrate that engineering lignin composition in poplar can improve saccharification, and emphasize the importance of construct design, translational research and field validation.

alternative lignin monomers↗

DOE OSTI2025

Biomarkers, Proteoforms, and Mass Spectrometry–Based Assays for Diabetes Clinical Research

Abstract The prevalence of diabetes, particularly type 2 diabetes, has reached epidemic proportions globally. The number of patients with type 1 diabetes (T1D) is also increasing rapidly. Despite advancements in understanding the pathogenesis of diabetes, the lack of circulating pancreatic biomarkers and reliable clinical-grade assays remains a major gap in diabetes research, often hindering the ability to adequately assess disease progression and therapeutic responses. This mini-review discusses emerging pancreatic biomarkers, with an emphasis on T1D, the limitations of current immunoassays, and the expanding role of mass spectrometry–based assays. Highlights include the recent work within the NIDDK-funded “Targeted Mass Spectrometry Assays for Diabetes and Obesity Research (TaMADOR)” consortium, which aims to develop robust, quantitative, and transferable assays for translational research. The review also emphasizes the importance of proteoform-specific assays for monitoring pancreatic function, including prohormone processing during disease progression or in responses to therapy.

Endocrinology & Metabolism↗

DOE OSTI2025

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Announcing the Biomedical Data Translator: Initial Public Release

ABSTRACT The growing availability of biomedical data offers vast potential to improve human health, but the complexity and lack of integration of these datasets often limit their utility. To address this, the Biomedical Data Translator Consortium has developed an open‐source knowledge graph–based system—Translator—designed to integrate, harmonize, and make inferences over diverse biomedical data sources. We announce here Translator's initial public release and provide an overview of its architecture, standards, user interface, and core features. Translator employs a scalable, federated, knowledge graph framework for the integration of clinical, genomic, pharmacological, and other biomedical knowledge sources, enabling query retrieval, inference, and hypothesis generation. Translator's user interface is designed to support the exploration of knowledge relationships and the generation of insights, without requiring deep technical expertise and gradually revealing more detailed evidence, provenance, and confidence information, as needed by a given user. To demonstrate Translator's application and impact, we highlight features of the user interface in the context of three real‐world use cases: suggesting potential therapeutics for patients with rare disease; explaining the mechanism of action of a pipeline drug; and screening and validating drug candidates in a model organism. We discuss strengths and limitations of reasoning within a largely federated system and the need for rich concept modeling and deep provenance tracking. Finally, we outline future directions for enhancing Translator's functionality and expanding its data sources. Translator represents a significant step forward in making complex biomedical knowledge more accessible and actionable, aiming to accelerate translational research and improve patient care.

Research & Experimental Medicine↗

DOE OSTI2025

Building High-Energy Silicon-Containing Batteries Using Off-The-Shelf Materials

The technology of silicon anodes appears to be reaching maturity, with high-energy Si cells already in pilot-scale production. However, the performance of these systems can be difficult to replicate in academic settings, making it challenging to translate research findings into solutions that can be implemented by the battery industry. Part of this difficulty arises from the lack of access to engineered Si particles and anodes, as electrode formulations and the materials themselves have become valuable intellectual property for emerging companies. Here, we summarize the efforts by Argonne’s Cell Analysis, Modeling, and Prototyping (CAMP) Facility in developing Si-based prototypes made entirely from commercially available materials. We describe the many challenges we encountered when testing high-loading electrodes (>5 mAh cm −2 ) and discuss strategies to mitigate them. With the right electrode and electrolyte design, we show that our pouch cells containing ≥ 70 wt% SiO x can achieve 600–1,000 cycles at C/3 and meet projected energy targets of 700 Wh L −1 and 350 Wh kg −1 . These results provide a practical reference for research teams seeking to advance silicon-anode development using accessible materials.

25 ENERGY STORAGE↗

DOE OSTI2025

Powering the Woods Hole X-Spar Buoy with Ocean Wave Energy—A Control Co-Design Feasibility Study

Despite its success in measuring air–sea exchange, the Woods Hole Oceanographic Institution’s (WHOI) X-Spar Buoy faces operational limitations due to energy constraints, motivating the integration of an energy harvesting apparatus to improve its deployment duration and capabilities. This work explores the feasibility of an augmented, self-powered system in two parts. Part 1 presents the collaborative design between X-Spar developers and wave energy researchers translating user needs into specific functional requirements. Based on requirements like desired power levels, deployability, survivability, and minimal interference with environmental data collection, unsuitable concepts are pre-eliminated from further feasibility study consideration. In part 2, we focus on one of the promising concepts: an internal rigid body wave energy converter. We apply control co-design methods to consider commercial of the shelf hardware components in the dynamic models and investigate the concept’s power conversion capabilities using linear 2-port wave-to-wire models with concurrently optimized control algorithms that are distinct for every considered hardware configuration. During this feasibility study we utilize two different control algorithms, the numerically optimal (but acausal) benchmark and the optimized damping feedback. We assess the sensitivity of average power to variations in drive-train friction, a parameter with high uncertainty, and analyze stroke limitations to ensure operational constraints are met. Our results indicate that a well-designed power take-off (PTO) system could significantly extend the WEC-Spar’s mission by providing additional electrical power without compromising data quality.

autonomous systems↗

DOE OSTI2025

Investigating the role of adipose tissue in mobility and aging: design and methods of the Adipose Tissue ancillary to the Study of Muscle, Mobility, and Aging (SOMMA-AT)

Background Age-related changes in adipose tissue affect chronic medical diseases and mobility disability but mechanism remains poorly understood. The goal of this study is to define methods for phenotyping unique characteristics of adipose tissue from older adults. Methods Older adults enrolled in study of muscle, mobility, and aging selected for the adipose tissue ancillary (SOMMA-AT; N = 210, 52.38% women, 76.12 ± 4.37 years) were assessed for regional adiposity by whole-body magnetic resonance (AMRA) and underwent a needle-aspiration biopsy of abdominal subcutaneous adipose tissue (ASAT). ASAT biopsies were flash frozen, fixed, or processed for downstream applications and deposited at the biorepository. Biopsy yields, qualitative features, adipocyte sizes, and concentration of adipokines secreted in ASAT explant conditioned media were measured. Inter-measure Spearman correlations were determined. Results Regional, but not total, adiposity differed by sex: women had greater ASAT mass (8.20 ± 2.73 kg, p < .001) and biopsy yield (3.44 ± 1.81 g, p < .001) than men (ASAT = 5.95 ± 2.30 kg, biopsy = 2.30 ± 1.40 g). ASAT mass correlated with leptin (r = 0.54, p < .001) and not resistin (p = .248) and adiponectin (p = .353). Adipocyte area correlated with ASAT mass (r = 0.34, p < .001), BMI (r = 0.33, p < .001), adiponectin (r = −0.22, p = .005) and leptin (r = 0.18, p = .024) but not with resistin (p = .490). Conclusion In addition to the detailed ASAT biopsy processing in this report, we found that adipocyte area correlated with ASAT mass, and both measures related to some key adipokines in the explant conditioned media. In conclusion, these results, methods, and biological repositories underscore the potential of this unique cohort to impact the understanding of aging adipose biology on disease, disability, and other aging tissues.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

City Climate Action Plans Through the Lens of The Food-Energy-Water Nexus

Interdependent resource management concepts originated in the mid-20th century, and the recent use of the term “nexus” highlights interconnected relationships among resources like food, energy, and water. United States and European science foundations have shown a growing interest in the food-energy-water (FEW) nexus, leading to increased research on their joint management. However, one major criticism of the FEW nexus is the limited translation of the research into practical policies and implementation, such as climate action plans. To assess the incorporation of FEW nexus principles into climate planning, we systematically evaluate 100 climate action plans from large U.S. cities. Our findings show that the most discussed FEW links involve energy, particularly the water-for-energy and food-for-energy connections, found in over half of the documents (56%). Our analysis promotes the integration of the FEW nexus into climate action plans while addressing the barriers to its effective implementation.

Mounir, Adil [Air Force Institute of Technology]↗

DOE OSTI2026

An mRNA vaccine encoding the Ebola virus glycoprotein induces high neutralizing antibody titers and provides strong protection against lethal infections in mouse models

Ebola virus (EBOV) is the causative agent of Ebola disease (EBOD), a viral hemorrhagic fever with a notably high case fatality rate. Current treatments for EBOD are limited to monoclonal antibodies or two licensed viral vector vaccines, a recombinant vesicular stomatitis virus (rVSV)-vectored vaccine or an adenovirus and modified vaccinia Ankara regimen. However, comparisons of protection, efficacy, and durability with alternative nucleotide platforms remain understudied. Here, we evaluated the immunogenicity of an mRNA vaccine expressing the EBOV glycoprotein (GP) in parallel with rVSV- and DNA-based vaccine platforms. The mRNA EBOV-GP vaccine, formulated in lipid nanoparticles, elicited significantly higher levels of total IgG and neutralizing antibody titers compared to the rVSV-EBOV-GP vaccine. Linear antibody epitope analysis indicated a preference for targeting the mucin-like domain in EBOV-GP1 following rVSV-based vaccination, while the mRNA platform distinctly targeted the internal fusion loop of EBOV-GP2. After characterizing the immunogenicity of the mRNA vaccine, two models of EBOD were used to demonstrate its protective efficacy: a surrogate rVSV-based challenge model of EBOD using type-I interferon deficient C57BL/6 mice and infection of BALB/c mice with authentic mouse-adapted EBOV. In both studies, the EBOV mRNA vaccine fully protected the mouse cohorts against morbidity and mortality. Additionally, the EBOV mRNA vaccine produced greater neutralizing antibody titers compared to the DNA EBOV-GP vaccine. These results suggest that an mRNA vaccine expressing EBOV-GP can induce robust, functional humoral responses that are protective against EBOD, warranting further development as an alternative to, or as part of a vaccine strategy including, viral vectored vaccines.

DNA vaccines↗

DOE OSTI2025

The Global Spectra-Trait Initiative: A database of paired leaf spectroscopy and functional traits associated with leaf photosynthetic capacity (v1.0.0)

The Global Spectra-Trait Initiative (GSTI) aims to generate generalizable spectra trait models using reflectance data to predict leaf traits associated with the photosynthesis capacity of leaves. It comprises a synthesized dataset of leaf trait data, input datasets and code. Leaf traits include the maximum carboxylation rate of rubisco (Vcmax), the maximum electron transport rate (Jmax), the dark respiration, as well as the prediction of leaf nitrogen, leaf mass per area (LMA), and leaf water content (LWC). The dataset comprises >7500 paired observations from around 400 species from a broad range of biomes. This dataset comprises a zip file of the GSTI GitHub repository (https://github.com/plantphys/gsti), the synthesized database (.csv) and database metadata files. This dataset was updated on 2025-12-12 with minor edits to mirror the accepted manuscript version and GitHub release (Version 1.0.0 (ESSD accepted version)). Edits included minor changes to the project documentation on GitHub and removal of 12 duplicate entries from the database.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles

Small-angle x-ray scattering (SAXS) is a powerful technique for assessing macromolecular structure. High-throughput SAXS is limited by the time-consuming and, at times, subjective nature of SAXS data interpretation. Here, we present SAXS Assistant, a Python-based script that streamlines SAXS data analysis to extract features for machine learning (ML) and key structural parameters, including the Guinier radius of gyration (R g ), pair distance distribution function (PDDF)-derived R g , maximum particle dimension (D max ), and Kratky plots. The script builds upon BioXTAS RAW and validates reliability via Guinier/PDDF R g agreement, an important indicator of well-measured data sets. For assistance in D max estimation, a multilayer perceptron regressor was trained with 1940 data files from the Small Angle Scattering Biological Data Bank. The model achieved a test set performance R 2 = 0.90 and mean absolute error = 11.7 Å. Training exclusively with experimental data translates analyses from researchers, including experts in the field, to the ML model, which helps assess D max estimations from PDDF. Gaussian mixture model clustering was implemented to classify profiles into structural classes based on entries in the Small Angle Scattering Biological Data Bank. Users may therefore assess the similarity between experimental samples and known biomolecular shapes within the mapped repository entries. This probabilistic clustering aids in quantifying information from Kratky and generating shape-descriptive features. SAXS Assistant accelerates SAXS data analysis through enforced quality control, ML-ready outputs, and flags for low-confidence results. In addition to providing the ability to analyze large data sets at high throughput, this tool is versatile and may serve researchers in both biological and synthetic polymer research fields.

36 MATERIALS SCIENCE↗

NASA NTRS2026

Polarized Resonant Soft X-ray Scattering (P-RSoXS) as a New Technique for Characterizing Amorphous Astromaterials

In the coming years, samples will be returned from several asteroids, the lunar surface, and the first material returned directly from the Martian surface. Previous in-situ and remote studies indicate these samples will contain abundant amorphous or weakly crystalline materials. However, detailed characterization and quantification of these amorphous materials remains challenging. Conventional techniques, including electron microscopy and X-ray diffraction, provide important information on material structure, but are generally limited to crystalline materials. Polarized resonant soft X-ray scattering (P-RSoXS) is a synchrotron-based X-ray scattering technique that has been used to characterize and quantify weakly crystalline systems, including soft materials. This research aims to translate this technique to geologic and extraterrestrial materials, building off the knowledge of using P-RSoXS to interrogate soft materials. P-RSoXS is well-suited to interrogate geologic materials, which are often multiphase, heterogeneous systems with crystalline and amorphous components, and domain sizes on the order of tens of nanometers. It is anticipated that future work will help reveal new chemical and structural information in geologic materials. Maturation of this preliminary work to develop P-RSoXS to characterize astromaterial-relevant samples will provide new understanding of the secondary processes responsible for the development of amorphous astromaterials and further elucidate our knowledge of the geologic history and past alteration processes.

Joshua H Litofsky↗

DOE OSTI2025

Meta‐Analysis and Regression Modeling of the Impacts of Four Indoor Environmental Quality Metrics on Office Performance

Awareness of how buildings interact with the occupant experience—especially human performance—is becoming more prevalent, as seen by increasing interest and investment in healthy built environments. However, there is a need to synthesize the wide array of existing indoor environmental assessment and performance research in a way that can translate directly to building design and operation. Existing research in this area typically focuses on a single isolated metric and has not focused on making the results utilizable by building practitioners. The aim of this research is to investigate existing office performance literature through meta‐analyses and produce regression models for four indoor environmental quality (IEQ) metrics to support critical decision‐making for building operation and renovation. To reach this aim, a literature review was conducted to identify studies that measure the impact of changing ventilation rate, temperature, horizontal illuminance, and noise level in offices on occupant task performance. This repository of field and laboratory studies was analyzed to visualize the trends between the selected IEQ metrics and task performance. The temperature, ventilation rate, and horizontal illuminance regression models showed clear improvement potential when modifying indoor conditions toward the defined high‐performance range, while the regression model for noise level was inconclusive. The discussion notes the importance of designing holistically for all components of these IEQ categories to utilize the results, for example, good filtration on outdoor air for quantifying ventilation impact and uniform overhead lighting with low contrast for quantifying horizontal illuminance impact. The novelty of this work is in considering multiple facets of the indoor environment under a single, unified analysis schema and producing IEQ‐based performance gains that can directly inform cost‐benefit analyses of building design and renovation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Decision-Model Supported Algal Cultivation Process Enhancement (DMSACPE) (Final Technical Report)

This project was proposed in response to the FY19 Bioenergy Technologies Office Multi-Topic Funding Opportunity Announcement DE-FOA-0002029, Area of Interest Subtopic 1 (AOI1): Cultivation Intensification Processes for Algae. The main challenge identified by the FOA was “in translating results between laboratory research systems and larger-scale outdoor (or mass culture) systems. These difficulties limit reliable experimental durations, adequate and representative experimental volumes of material, and results that can be reproduced reliably. By overcoming the challenge in translating results between laboratory and mass cultures, the objective of AOI 1 is to increase the harvest yield, robustness, and quality of algae cultivation for biofuels and bioproducts”. This report marks the final technical deliverable of this project and reviews all the major findings of the research program.

09 BIOMASS FUELS↗

DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

Ridesharing allows people to share a vehicle with others traveling in the same direction, which can reduce costs and traffic congestion. Pooled rideshare (PR) services, such as UberX Share and Lyft Shared, offer an economical and environmentally friendly alternative by matching passengers traveling similar routes. However, despite these benefits, PR adoption remains low due to concerns about safety, privacy, and convenience. This research explores the factors influencing PR adoption and provides recommendations to improve user acceptance. A nationwide survey of 5,385 participants across the U.S. was conducted to understand why people choose or avoid PR. The study identified five key factors influencing PR consideration: safety, service experience, privacy, traffic/environment, and time/cost. Additional research examined ways to optimize PR experiences by identifying four critical factors: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. To measure the impact of these factors, a statistical model called the Pooled Rideshare Acceptance Model (PRAM) was developed, providing insights into how each element influences PR adoption. Further analysis using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) revealed how demographic characteristics such as age, gender, income, and past rideshare experience shape PR perceptions. Some key findings from the multigroup analyses showed that younger users valued technological features and environmental benefits, while older users prioritized reliability and service transparency. Additionally, privacy concerns were more significant for female users, while convenience was critical for higher-income groups. These results emphasize that a 'onesize-fits-all' approach to PR service design is not effective, highlighting the need for tailored strategies to address different user segments. Further, workshops were conducted with researchers and students to translate the findings into real-world solutions. These workshops and 3 all the statistical analyses led to the development of 95 actionable recommendations. The recommendations focus on key areas such as safety, service reliability, user education, and accessibility, offering tangible improvements to PR services. The insights from this study provide valuable guidance for policymakers, transportation network companies (TNCs), and researchers aiming to make PR services safer, more accessible, and widely accepted. By addressing user concerns, PR can become a more viable transportation option, supporting sustainable urban mobility and reducing reliance on private vehicles. Additionally, these findings emphasize the importance of user-centric service design in encouraging broader PR adoption. Future research should explore evolving trends in PR preferences, technological advancements, and policy changes to ensure continued improvements. By implementing these recommendations, PR services can better align with user expectations, enhance trust in shared mobility, and contribute to a more efficient transportation ecosystem.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Lost in translation: What we have learned from attributes that do not translate from Arabidopsis to other plants

Abstract Research in Arabidopsis thaliana has a powerful influence on our understanding of gene functions and pathways. However, not everything translates from Arabidopsis to crops and other plants. Here, a group of experts consider instances where translation has been lost and why such translation is not possible or is challenging. First, despite great efforts, floral dip transformation has not succeeded in other species outside Brassicaceae. Second, due to gene duplications and losses throughout evolution, it can be complex to establish which genes are orthologs of Arabidopsis genes. Third, during evolution Arabidopsis has lost arbuscular mycorrhizal symbiosis. Fourth, other plants have evolved specialized cell types that are not present in Arabidopsis. Fifth, similarly, C4 photosynthesis cannot be studied in Arabidopsis, which is a C3 plant. Sixth, many other plant species have larger genomes, which has given rise to innovations in transcriptional regulation that are not present in Arabidopsis. Seventh, phenotypes such as acclimation to water stress can be challenging to translate due to different measurement strategies. And eighth, while the circadian oscillator is conserved, there are important nuances in the roles of circadian regulators in crop plants. A key theme emerging across these vignettes is that even when translation is lost, insights can still be gained through comparison with Arabidopsis.

Biochemistry & Molecular Biology↗

DOE OSTI2026

Electrolyte Design for Fast‐Charging Lithium‐Based Batteries

Fast charging is essential for the widespread adoption of lithium (Li)-ion batteries, but it is fundamentally limited by sluggish interfacial kinetics, Li plating, and electrolyte instability at high current densities. Over the past decade, electrolyte engineering has emerged as a key strategy to address these challenges. This review summarizes the development of fast-charging electrolytes over the past ten years and outlines a design framework. Electrolyte formulations are first deconstructed into their main components—solvents, salts, and functional additives—and representative strategies for tuning solvation structure and interphase chemistry are discussed to suppress Li plating and improve interfacial kinetics. The discussion then extends to advanced electrolyte systems, particularly localized high-concentration electrolytes (LHCEs), and their compatibility with different anode chemistries. Advanced characterization techniques are also summarized and categorized based on destructiveness, spatial and temporal resolution, quantitative analysis, and the chemical species or processes probed across multiple length scales. Recent progress in AI-enabled electrolyte discovery and battery management system (BMS) strategies for optimized fast-charging protocols is further highlighted. Finally, perspectives are presented on translating electrolyte innovations from academic research to practical applications, with emphasis on cell format, realistic operating conditions, and manufacturability.

25 ENERGY STORAGE↗