Biopolymer-based emulsions for the stabilization of Trichoderma atrobrunneum conidia for biological control
Bibliographic record
Abstract
Trichoderma spp. are ubiquitous soil-borne fungi that are widely used in biological control to promote and regulate healthy plant growth, as well as protect against plant pathogens. However, as with many biological materials, the relative instability of Trichoderma propagules limits its practical use in industrial applications. Therefore, there has been significant research interest in developing novel formulations with various carrier substances that are compatible with these fungal propagules and can enhance the shelf-life and overall efficacy of the Trichoderma. To this end, herein, we investigate the use of a variety of biopolymers and nanoparticles for the stabilization of Trichoderma atrobrunneum T720 conidia for biological control. The best-performing agents-agar and cellulose nanocrystals (CNC)-were then used in the preparation of oil-in-water emulsions to encapsulate conidia of T720. Emulsion properties including oil type, oil:water ratio, and biopolymer/particle concentration were investigated with respect to emulsion stability, droplet size, and viability of T720 conidia over time. Overall, agar-based formulations yielded highly stable emulsions with small droplet sizes, showing no evidence of drastic creaming, or phase separation after 1 month of storage. Moreover, agar-based formulations were able to maintain ~ 100% conidial viability of T720 after 3 months of storage, and over 70% viability after 6 months. We anticipate that the results demonstrated herein will lead to a new generation of significantly improved formulations for practical biological control applications. KEY POINTS: • Various biopolymers were evaluated for improving the stability of Trichoderma conidia • Oil in water emulsions was prepared using cellulose nanocrystals and agar as interface stabilizers • Agar-based emulsions showed ~ 100% viability for encapsulated conidia after 3 months of storage.
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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 | 0.001 | 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".