Formic Acid Pursues Efficient Hydrodeoxygenation of Naphthols and Phenolic Derivatives to Arenes
Bibliographic record
Abstract
The use of high-pressure hydrogen poses significant challenges, including safety risks, storage problems, and elevated costs. Consequently, developing reductive chemical processes utilizing low-pressure hydrogen is highly appealing for industrial-scale applications. This emphasizes the critical need for sustainable alternatives that offer safer and more accessible reaction conditions. Liquid organic hydrogen carriers (LOHCs) are appealing materials due to their capability to generate hydrogen in situ, which can be directly utilized to produce target biofuel precursors, fuels, or fuel additives. Hydrodeoxygenation (HDO) is an efficient approach for transforming lignin and its phenolic derivatives into valuable aromatic chemicals and fuels. Herein, we present an alternative HDO of naphthols and phenols using a commercial heterogeneous catalyst, Pd/C, employing formic acid as LOHC. This paper presents a consistent substrate scope for the HDO of different naphthols and phenols, including pharmaceutically relevant molecules such as amylmetacresol and menthol.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".