Kinetic Study of Photoelectrochemical Oxidation of Lignin Model Compounds on TiO<sub>2</sub> Nanotubes
Bibliographic record
Abstract
In this study, TiO2 nanotubes were prepared via the electrochemical oxidation of titanium substrates in a non-aqueous electrolyte and their morphology and microstructures were examined by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The photoelectrochemical oxidation of two lignin model compounds, 1-(3,4-dimethoxyphenoxy)-2-(2-methoxyphenoxy)-1,3-propanediol (DMP) and 3-hydroxy-1-(3,4-dimethoxyphenoxy)-2-(2-methoxyphenoxy)-1,3-propanone (HDM), was investigated. A new band appeared at ~304 nm during the photoelectrochemical oxidation of DMP. The rate of DMP intermediate formation was amplified with the increase of initial concentrations, while it was diminished with increased temperature. Despite the similarity in structure between HDM and DMP, there are only small increases in absorbance during the oxidation of HDM, suggesting that HDM is less reactive. Quantum chemical calculations based on the density functional theory (DFT) were performed in order to link photoelectrochemical reactivity with specific molecular properties. Relatively higher ELUMO-EHOMO of HDM makes it more stable and thus more refractory to oxidation, which is consistent with our photoelectrochemical results.
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".