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Record W4391613400 · doi:10.1002/ese3.1690

Progress in energy: USA–Canada special issue on energy

2024· article· en· W4391613400 on OpenAlexaboutno aff
Yun Hang Hu

Bibliographic record

VenueEnergy Science & Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsnot available
Fundersnot available
KeywordsEnergy (signal processing)Engineering physicsEngineeringPhysics

Abstract

fetched live from OpenAlex

The current energy landscape mainly encompasses fossil fuels, renewable energy, and nuclear energy.1-8 CO2-related concerns have garnered significant research and development focus within the field of fossil fuels.7, 9-11 The development of materials and technologies plays an important role in enhancing energy efficiency and reducing the cost for renewable energy, such as solar energy and bioenergy.12-29 In the realm of nuclear energy, the fission nuclear power continues to be an essential part of low-carbon electricity generation, but more efforts are needed for the development of advanced reactors, the fabrication of nuclear fuels, the disposal of nuclear wastes, and the restoration of nuclear environment.30-32 To promote the research and development efforts for energy, ESE is going to publish a series of special issues from different countries. This is the USA–Canada special issue. The first article in this special issue is a review on rock thermal energy storage (RTES) written by Sasmito and co-workers. Thermal energy storage is essential for the utilization of thermal energy. RTES, as a thermal storage technology, has garnered notable attention owing to its advantages, including large energy storage capacity, a straightforward storage mechanism, and cost-effectiveness. However, it also faces technical challenges, such as the pressure drop across the storage system and the nonoptimal heat transfer between the heat transfer fluid and the storage medium. Addressing these challenges has spurred intensive research and development efforts. This article not only provides a comprehensive review of the advancements in RTES but also discusses nontechnical aspects, including policy and regulations. Zhang and colleagues conducted a comprehensive review of road transportation, with a particular focus on decarbonization in the second article. Achieving ultralow CO2 emissions from modern road transportation is imperative. The well-to-wheel greenhouse gas emissions of a propulsion system hinge on two critical factors: the energy efficiency of the system and the carbon intensity of the energy source. This article offers a detailed review of propulsion systems for transportation and explores the decarbonization potential inherent in various propulsion technologies. The synthetic fuel, capable of meeting all Jet A-1 specifications, has the potential for use as a jet fuel. Fischer–Tropsch-synthesized paraffinic kerosene plus aromatics (FT SPK/A) can, in principle, qualify as Jet A-1. However, significant obstacles remain in the journey toward obtaining global approval for fully formulated synthetic jet fuel. In the third article, Klerk et al. explore various refining pathways that can be utilized to produce FT SPK/A, highlighting the practicality of generating a fully formulated jet fuel through Fischer–Tropsch refining. CO serves as a crucial feedstock in the chemical industry, contributing to the production of high-value chemicals and clean fuels. This has sparked significant research interest in developing efficient CO conversion processes. Redox potential is a key thermodynamic quantity in these processes, but only standard reduction potentials at 25°C and 1 atm are available. In the last article, we reveal the effects of temperature (0–1000°C), pressure (1–100 atm), and adsorption on the redox potentials of 18 CO conversion reactions to the formation of various compounds. The predicted reduction potentials at different temperatures and pressures can be applied in the design of innovative processes. In conclusion, I express my gratitude to all authors, reviewers, and the dedicated Wiley staff for their invaluable contributions to the publication of this special issue. Professor Yun Hang Hu is the Editor-in-Chief of Energy Science & Engineering.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.987
Threshold uncertainty score0.536

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0040.001
Scholarly communication0.0060.004
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.1250.044

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 head0.229
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreEditorial

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
Published2024
Admission routes1
Has abstractyes

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