Hydrothermal aging of moso bamboo: Degradation mechanisms and storage life prediction
Bibliographic record
Abstract
The fluctuations of storage temperature and humidity detrimentally affect the bamboo quality and longevity, making it crucial to investigate. Herein, we explored the physical and mechanical properties of moso bamboo ( Phyllostachys edulis ) subjected to 100-day moist heat cycling aging (MHCA-1: transitioning from low-temperature/high-humidity to high-temperature/low-humidity; MHCA-2: transitioning from low-temperature/low-humidity to high-temperature/high-humidity; CHT: 25 °C-constant temperature and 60% relative humidity) alongside a control group. Employing a multiscale characterization and Random Forest (RF) modeling, we evaluated the impacts of temperature and humidity fluctuations on the bamboo quality, and the influence mechanism of storage conditions on its physical and mechanical properties were elucidated. Results indicated that elevated temperature and humidity led to remarkable fluctuation in bamboo moisture (from −20.36% to 32.99%), weight gain (from −32.69% to 6.19%), and dimensional expansion (from −5.37% to 2.38%). Conversely, high-temperature and low-humidity drying conditions resulted in moisture loss and dimensional shrinkage. Total color difference (TCD) of bamboo cortex followed the order: MHCA-2 (7.46) < CHT (12.24) < MHCA-1 (20.10) < control (22.63). The TCD of bamboo pith positively was related with storage temperature. Periodic moist heat aging induced the permanent deformation in bamboo, reducing its elastic modulus by 30.05%–43.79%. Under moist heat aging conditions, the characteristic hemicellulose functional groups, including hydroxyl (−OH), carbonyl ( C = O ), ether (C–O–C), and aromatic C = C moieties exhibited remarkable structural modifications, i.e., peak weakening, shifting, or morphological alterations in Fourier transform infrared (FT-IR) spectra. Additionally, these conditions elevated the thermal decomposition onset temperature of cellulose while decreasing its peak intensity. Overall, the RF modeling approach demonstrated a high accuracy in predicting bamboo behavior under varying moisture-heat conditions. It improved bamboo storage and recycling by supporting sorting and grading with reliable long-term data.
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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".