Mapping Scientific and Topic Evolution Around Lithium-Based Clean Energy Technologies: A Bibliometric Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change effects have a significant global negative impact, prompting global leaders to promote clean energy use to reduce carbon emissions. Electric vehicles powered by lithium-ion batteries are crucial to achieving this goal. Lithium is an essential material for the efficient operation of electric batteries, so in recent years, its demand has increased, and it is considered a strategic mineral. This paper aims to describe and analyze the scientific development of lithium-based clean energy technologies and reveal future areas of scientific production priority. This research is conducted through a bibliometric analysis in the Scopus database from 1929 to April 2024. Using the software Bibliometrix 4.1 and Biblioshiny the exported literature data are analyzed. The number of papers on lithium topics has significantly increased since 2018, with China leading in publications and collaborating with many countries. The trending topics are geological prospection, lithium ore characterization, chemical engineering, and lithium energy technologies. Lithium research is a growing field, but its development is uneven. Only a few countries lead in scientific production and lithium energy technologies, and sustainability lithium topics related to Life-Cycle Analysis (LCA) require further attention. Lithium research development is influenced by global economic trends.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.033 | 0.115 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it