Characterizing spatial distribution of the adsorbed water in wood cell wall of <i>Ginkgo biloba</i> L. by μ-FTIR and confocal Raman spectroscopy
Bibliographic record
Abstract
Abstract The adsorbed water influences significantly, the physical and mechanical properties of wood. In the present paper, the spatial distribution of adsorbed water in wood cell walls has been studied by μ-Fourier transform infrared (μ-FTIR) and confocal Raman spectroscopy. In situ μ-FTIR spectra were collected from three randomly selected areas in different cell wall regions, which were exposed to an environment with 0% to 96% relative humidity (RH). The water adsorption sites were easily detectable based on OH, C=O, and C-O group vibrations and it was shown that the adsorbed water concentration was not uniform in different regions. Confocal Raman spectroscopy images were collected from the cell corner (CC) and middle layer of the secondary wall (S2) and the non-uniformity of water distribution could also be confirmed by this approach. It was demonstrated that both μ-FTIR and confocal Raman spectroscopy provide valuable information about the spatial distribution of adsorbed water in morphologically distinct cell wall regions.
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".