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Record W4399053584 · doi:10.1002/eem2.12760

Harvesting Energy Via Water Movement and Surface Ionics in Microfibrous Ceramic Wools

2024· article· en· W4399053584 on OpenAlexaff
Manpreet Kaur, Avinash Alagumalai, Omid Mahian, Sameh M. Osman, Tadaaki Nagao, Zhong Lin Wang

Bibliographic record

VenueEnergy & environment materials · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCeramicMovement (music)Materials scienceComposite materialPhysicsAcoustics

Abstract

fetched live from OpenAlex

Due to the push for carbon neutrality in various human activities, the development of methods for producing electricity without relying on chemical reaction processes or heat sources has become highly significant. Also, the challenge lies in achieving microwatt‐scale outputs due to the inherent conductivity of the materials and diverting electric currents. To address this challenge, our research has concentrated on utilizing nonconductive mediums for water‐based low‐cost microfibrous ceramic wools in conjunction with a NaCl aqueous solution for power generation. The main source of electricity originates from the directed movement of water molecules and surface ions through densely packed microfibrous ceramic wools due to the effect of dynamic electric double layer. This occurrence bears resemblance to the natural water transpiration in plants, thereby presenting a fresh and straightforward approach for producing electricity in an ecofriendly manner. The generator module demonstrated in this study, measuring 12 × 6 cm 2 , exhibited a noteworthy open‐circuit voltage of 0.35 V, coupled with a short‐circuit current of 0.51 mA. Such low‐cost ceramic wools are suitable for ubiquitous, permanent energy sources and hold potential for use as self‐powered sensors and systems, eliminating the requirement for external energy sources such as sunlight or heat.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.006
GPT teacher head0.176
Teacher spread0.170 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2024
Admission routes1
Has abstractyes

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