Effect of Glow-Discharge Plasma Treatment on Contact Angle and Micromorphology of Bamboo Green Surface
Bibliographic record
Abstract
The inner and outer surfaces of bamboo stems are usually removed prior to the manufacture of bamboo panels because the surfaces are hydrophobic and difficult to bond with glue. Hence, the recovery and utilization ratio of bamboo during processing is low. This study focused on using glow-discharge plasma to treat green bamboo surfaces to make them less hydrophobic. The effects of plasma treatment on green bamboo stems were examined using contact goniometry (wettability), non-contact confocal profilometry and scanning electron microscopy (SEM). Confocal profilometry and SEM revealed that the morphology of green bamboo surfaces varied between 3 different stems. Plasma was able to etch bamboo green surfaces, and make them rougher and more powdery. Plasma treatment was effective at converting green bamboo surfaces from hydrophobic (initial contact angle >110°) to hydrophilic (contact angle <20°). However, this effect was temporary and contact angle increased with time and recovered approximately 30% of its original value after 24 h. Based on our findings, we conclude that plasma treatment can alter parameters such as surface energy and roughness that could improve glue bonding of green bamboo, but delays between plasma treatment and further processing would need to be minimized.
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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 | 0.000 | 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".