Bibliographic record
Abstract
Thermoelectric (TE) materials can directly convert heat into electricity based on the Seebeck effect, offering high durability and reliability with no fluids or moving parts.TE energy conversion is therefore considered to be one of the most promising transformative technologies for the reduction of global energy consumption.The past two decades have witnessed the rapid growth of thermoelectric research, including optimization of existing TE materials and exploitation of new materials.In the past five years, according to Web of Science statistics from June 2019, approximately 1500-2000 articles in TEs were published annually, in which the number of publications in organic and hybrid TEs rose significantly from 200 in 2015 to 366 in 2018.Despite this increasing research thrust, to the best of our knowledge there has so far been no specific organic and hybrid TE special issue published in a primary research journal.Motivated to fill this gap, we invited international leading experts in prime areas to present a range of research articles, from Reviews and Progress Reports to Full Papers and Communications, that summarize recent advances and offer insights into resolving current challenges.This themed issue covers organic and hybrid TE materials and applications, comprising chemical solution methods, nanoscale fabrication techniques, morphological investigation, computational studies of band structures, electrical and phonon transportation, and TE module applications.Conventional inorganic TE materials, among which more than ten families exhibit figure-of-merit zT values exceeding 1, have been extensively studied.Nevertheless, it has been difficult to realize large-scale commercial applications with inorganic TEs due to their intrinsic brittleness and toxicity.Furthermore, exciting breakthroughs in organic TEs have recently been made.They exhibit unique advantages over their inorganic counterparts, such as low weight, mechanical flexibility, rational design
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".