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Record W4414617202 · doi:10.1002/adma.202513255

Comparative Advances in Sulfide and Halide Electrolytes for Commercialization of All‐Solid‐State Lithium Batteries

2025· review· en· W4414617202 on OpenAlexaff
Mohamed Djihad Bouguern, Ningaraju Gejjiganahalli Ningappa, Karthik Vishweswariah, Anil Kumar M R, Ryoji Kanno, Karim Zaghib

Bibliographic record

VenueAdvanced Materials · 2025
Typereview
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsHalideSulfideElectrolyteLithium (medication)Ionic conductivityEnergy storageFast ion conductorCathodeOxide

Abstract

fetched live from OpenAlex

Abstract All‐solid‐state lithium batteries (ASSBs) outperform lithium‐ion batteries (LIBs) in safety, energy density, and thermal stability. Their performance depends on high ionic conductivity, chemical/physical stability, and scalable manufacture of solid electrolytes (SEs). This study compares sulfide‐ and halide‐based SEs, two promising next‐generation energy storage options. Soft mechanics permit sulfides with high room‐temperature conductivity, low activation energies, and processability, but high‐voltage cathode instability, moisture sensitivity, and probable hydrogen sulfide (H 2 S) release. Market prospects are favorable as the industry improves crystallinity and elemental substitution, especially for automotive cells. Chloride‐based halides are more environmentally friendly, have adequate voltage stability, and can be used with oxide cathodes without coatings. Despite traditionally low conductivity, high‐entropy, and oxyhalide chemistries currently reach 10 mS cm −1 , and scalable solvent syntheses and dry processing are driving adoption. Mechanical compliance and the use of rare elements (In, Sc) continue to cause integration and cost issues. Composition, microstructure, synthesis techniques, interfacial behavior, mechanical characteristics, and scalability are evaluated. The findings show sulfides have better conductivity and Li‐metal compatibility, but halides are more stable and manufacturable, recommending hybrid or tailored material selection based on application. Optimizing ASSB systems requires complementary sulfide/chloride utilization due to halides' mechanical constraints.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.744
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.338
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2025
Admission routes1
Has abstractyes

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