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Record W6992693173

Metallic Encapsulation for High Temperature (>500 °C) Thermal Energy Storage Applications

2015· article· en· W6992693173 on OpenAlexfundno aff

Bibliographic record

VenueDigital Commons - University of South Florida (University of South Florida) · 2015
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsnot available
FundersAdvanced Research Projects AgencyAdvanced Research Projects Agency - EnergyCanada Excellence Research Chairs, Government of Canada
KeywordsEncapsulation (networking)Thermal energy storageEnergy storageThermalCorrosionSustainable energyConcentrated solar powerSolar energyThermal energy
DOInot available

Abstract

fetched live from OpenAlex

Deployment of high temperature (>500 °C) thermal energy storage in solar power plants will make solar power more cost competitive and pave the way towards a sustainable future. In this research, a unique metallic encapsulation has been presented for thermal energy storage at high temperatures, capable of operation in aerobic conditions. This goal was achieved by employing low cost materials like carbon steel. The research work presents the unique encapsulation procedure adopted, as well as various coatings evaluated and optimized for corrosion protection. Experimental testing favored the use of 150 μm of nickel on carbon steel for corrosion protection in these conditions. These metallic encapsulations survived several thermal cycles at temperatures from 580 °C to 680 °C with one encapsulation surviving for 1700 thermal cycles.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.029
GPT teacher head0.198
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 designQualitative
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

Citations0
Published2015
Admission routes1
Has abstractyes

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