IUPAC Announces the 2024 Top Ten Emerging Technologies in Chemistry
Bibliographic record
Abstract
Abstract IUPAC has released the 2024 Top Ten Emerging Technologies in Chemistry. The goal of this initiative is to showcase the transformative value of chemistry and to inform the general public about the potential of chemical sciences to foster the well-being of Society and the sustainability of our planet. The Jury The following comprised the panel of judges for the 2024 Top Ten Emerging Technologies in Chemistry: Chair, Michael Droescher, (German Association for the Advancement of Science and Medicine), Jorge Alegre-Cebollada (Centro Nacional de Investigaciones Cardiovasculares, Spain), Mamia El-Rhazi, (Université Hassan II de Casablanca, Mohammedia, Morocco), Ehud Keinan (Technion, Israel), Javier García Martínez (Universidad de Alicante, Spain), Rai Kookana (CSIRO Land & Water, Australia), Juliane Sempionatto (Caltech, USA), Molly Shoichet (University of Toronto, Canada), Zhigang Shuai (Tsinghua University, China), Natalia P. Tarasova (D. I. Mendeleev University of Chemical Technology, Russia), Kira Welter (Wiley-VCH, Germany), and Bernard West (Life Sciences Ontario, Canada) —an international panel of scientists with a varied and broad range of expertise- reviewed and discussed the diverse pool of nominations of emerging technologies submitted by researchers from around the globe and selected the final top ten, covering a range of fields from synthesis and polymer chemistry to health and machine learning. These technologies are defined as transformative innovations in between a discovery and a fully-commercialized technology, having outstanding potential to open new opportunities in chemistry, sustainability, and beyond.
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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 | 0.048 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.028 | 0.008 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.035 | 0.023 |
| Insufficient payload (model declined to judge) | 0.036 | 0.032 |
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".