SELECTION OF A METHOD FOR STERILIZATION OF NANOSTRUCTURED BIOMATERIAL BASED ON BACTERIAL CELLULOSE
Bibliographic record
Abstract
Nanocomposites based on bacterial cellulose (BC), hydroxyapatite (HAp), and MXene represent a promising class of biomaterials for regenerative medicine and photothermal technologies due to their combination of biocompatibility, mechanical stability, electrical conductivity, and strong infrared absorption capability. However, the high sensitivity of MXene phases and the cellulose matrix to physicochemical impacts requires careful selection of sterilization approaches that provide sterility without substantial loss of key functional properties. This study presents a comparative evaluation of nine sterilization methods – autoclaving, dry heat, ethylene oxide treatment, γ-irradiation, electron beam (E-beam) irradiation, ultraviolet irradiation, ozonation, supercritical CO₂ treatment, and cold plasma – applied to BC/HAp/MXene nanocomposites. Sterility was assessed by direct plating on nutrient media; sterilization efficiency was calculated based on microbial growth. Complete sterility was achieved for all methods except UV (50%) and CO₂ treatment (90%). The effects of sterilization on material structure and properties were analyzed using SEM imaging, porosity measurement, mechanical testing, electrical conductivity assessment, and photothermal activity evaluation. Thermal treatment and ozonation caused the most pronounced structural damage and reduction in mechanical strength. Radiation-based methods induced moderate changes, whereas ethylene oxide, supercritical CO₂, and cold plasma treatment resulted in minimal morphological degradation. Cold plasma and EtO preserved up to 94–98% of the initial conductivity and photothermal response, while thermal treatment led to significant loss of functional performance due to thermo-oxidative degradation of MXene. Comprehensive analysis demonstrated that cold plasma and ethylene oxide provide the best balance between sterility and preservation of structural–functional properties of BC/HAp/MXene nanocomposites. These methods are the most promising for preparing such materials for biomedical applications.
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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) | 0.001 | 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".