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Record W2732191052 · doi:10.5539/enrr.v7n3p27

Applying Biomimetic Principles to Thermoelectric Cooling Devices for Water Collection

2017· article· en· W2732191052 on OpenAlexvenueno aff
Kyle B Davidson, Bahram Asiabanpour, Zaid Almusaied

Bibliographic record

VenueEnvironment and Natural Resources Research · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsnot available
FundersTexas State UniversityU.S. Department of Agriculture
KeywordsThermoelectric coolingThermoelectric effectEconomic shortageWater coolingEnvironmental scienceProcess engineeringThermoelectric materialsComputer scienceBiochemical engineeringMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The shortage of freshwater resources in the world has developed the need for sustainable, cost-effective technologies that can produce freshwater on a large scale. Current solutions often have extensive manufacturing requirements, or involve the use of large quantities of energy or toxic chemicals. Atmospheric water generating solutions that minimize the depletion of natural resources can be achieved by incorporating biomimetics, a classification of design inspired by nature. This research seeks to optimize thermoelectric cooling systems for use in water harvesting applications by analyzing the different factors that affect surface temperature and water condensation in TEC devices. Further experiments will be directed towards developing a robust, repeatable system, as well as an accurate measurement system. Surface modifications, device structure and orientation, and power generation will also be studied to better understand the ideal conditions for maximum water collection in thermoelectric cooling systems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.329
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations3
Published2017
Admission routes1
Has abstractyes

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