Tribute to John B. Goodenough: From Magnetism to Rechargeable Batteries
Bibliographic record
Abstract
Abstract In macroeconomics, the rate of economic growth in the world is a historical phenomenon, which will mark the 20th century. It was made possible by an increasing consumption of fossil fuel. However, not only are these resources limited and unrenewable, but combustion has dramatic side effects such as emission of greenhouse gases responsible for global warming and change of climate. As a consequence, major efforts have been taken to move away from fossil fuels and switch to renewable energy including solar and wind energy. These energy sources, however, are intermittent and can be integrated into the electrical network only after regulation. Electrochemical storage is a key solution to regulate these intermittent sources of energy into smart grids. Although governments have only recently become aware of this problem, some scientists have been focusing their attention on rechargeable batteries since the 1970s, and it took until 2019 for the Nobel committee to award the Nobel prize to those who paved the path to their development. John B. Goodenough is one of the three winners of this Nobel prize, for his pioneering research on lithium‐ion batteries (LIBs). The impact of LIBs includes the development of rechargeable hybrid and electric vehicles at the expense of gasoline cars. Before the 1970s, however, John B. Goodenough had already made major contributions to materials science as a solid state physicist, including the investigation of the interplay between the magnetic and transport properties of perovskites. It is the purpose of the present work to report a brief review of the scientific works of John B. Goodenough, through selected works that demonstrate his mastery of chemistry and physics.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".