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Record W4392832720 · doi:10.54097/xcq2s902

Sustainability Study based on Molten Salt Energy Storage

2023· article· en· W4392832720 on OpenAlexaff
Yusong Li, Yuheng Liu, Yuhao Lu, Ningyue Yu

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsDurham College
Fundersnot available
KeywordsMolten saltEnergy storageThermal energy storageEnvironmental scienceEnergy technologySustainabilityScope (computer science)Waste managementProcess engineeringEngineeringMaterials sciencePower (physics)Computer scienceElectrical engineeringMetallurgy

Abstract

fetched live from OpenAlex

The development of molten salt energy storage technology began in the 20th century, with the American ORNL first exploring the application of molten salt heat storage in the 1950s while experimenting with nuclear-powered aircraft. In the following decades of research and development, the scope of application of molten salt energy storage technology was expanded, not only by considering the heat storage function of molten salts but also by combining molten salt energy storage technology with clean energy (such as photovoltaic) to form the power station that has been used in many countries. In the 21st century, sustainable development research is becoming more and more popular, and molten salt energy storage technology is in line with the characteristics of sustainable development. This paper explores the suitability of molten salt energy storage technology for sustainable development by introducing molten salt energy storage technology, and then describes the strength of this technology such as non-pollution, low cost, and high efficiency, demonstrating energy storage potential through molten salts within sustainable development. At the same time, the hidden dangers and limitations of the current molten salt energy storage technology are also discussed dialectically.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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 designSimulation or modeling
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

Citations1
Published2023
Admission routes1
Has abstractyes

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