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Record W4408148104 · doi:10.1021/acs.chemrev.4c00850

Connecting the Dots: Sintering of Liquid Metal Particles for Soft and Stretchable Conductors

2025· review· en· W4408148104 on OpenAlexaff
Simok Lee, Syed Ahmed Jaseem, Nurit Atar, Meixiang Wang, Jeong Yong Kim, Mohammadreza Zare, Soo‐Young Kim, Michael D. Bartlett, Jae‐Woong Jeong, Michael D. Dickey

Bibliographic record

VenueChemical Reviews · 2025
Typereview
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsKootenay Association for Science & Technology
FundersNational Research Foundation of Korea
KeywordsChemistrySinteringNanotechnologyElectrical conductorComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

This review focuses on the sintering of liquid metal particles (LMPs). Here, sintering means the partial merging or connecting of particles (or droplets) to form a network of percolated and, thus, conductive electrical pathways. LMPs are attractive materials because they can be suspended in a carrier fluid to create printable inks or distributed in an elastomer to create soft, stretchable composites. However, films and traces of LMPs are not typically conductive as fabricated due to the native oxide that forms on the surface of the particles. In the case of composites, polymers can also get between particles, making sintering more challenging. Sintering can be done via a variety of ways, such as mechanical, thermal, and chemical processing. This review discusses the mechanisms to sinter these particles, patterning techniques that use sintering, unique properties of sintered LMPs, and their practical applications in fields such as stretchable electronics, soft robotics, and active materials.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.073
GPT teacher head0.329
Teacher spread0.256 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations87
Published2025
Admission routes1
Has abstractyes

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