From Bench to Greenhouse: The Comparative Nano-Bio System Effects of Green-Synthesized TiO2-NPs and Plant-Growth-Promoting Microorganisms in Capsicum annuum
Bibliographic record
Abstract
In this study, titanium dioxide nanoparticles (TiO2-NPs) were produced via green routes using blueberry extracts obtained with isopropanol (I-TiO2-NPs) and methanol (M-TiO2 NPs). HPLC-DAD confirmed phenolic/flavonoid profiles in the extracts, and spectroscopy/microscopy established anatase, polyhedral, mesoporous TiO2-NPs with Eg ≈ 3.0 eV, hydrodynamic sizes ≈ 130–150 nm and negative ζ-potentials (−33 to −50 mV). The in vitro compatibility between TiO2-NPs and the plant-growth-promoting microorganisms (PGPMs) Bacillus subtilis (Bs), Bacillus thuringiensis (B), and Trichoderma harzianum (T) sustained increased growth up to 150 µg/mL without visible negative effects. In greenhouse experimentation of Capsicum annuum exposed to low-moderate TiO2-NPs, an increased leaf number and plant height were observed, while root length did not exceed the controls. I-TiO2 at moderate concentrations, particularly with a single PGPM (B or T), promoted fresh and dry biomass accumulation. Biochemically, peroxidase rose sharply for M-TiO2 at a low dose with consortium, whereas I-TiO2 elicited broader antioxidant responses; total protein increased at higher doses for both formulations, and total chlorophyll was highest with I-TiO2 (high dose with or without PGPMS). Collectively, the nano-bio system shows a formulation- and dose-dependent biphasic behavior: (I) I-TiO2 enhances biomass and photosynthetic pigments; (II) M-TiO2 favors strong POX induction under specific microorganism-dose combinations; and (III) single PGPM co-application with I-TiO2-NPs or M-TiO2 NPs outperforms consortia under our experimental conditions. Green synthesis thus provides surface functionalities that improve dispersion, microbial compatibility, and predictable physiological/biochemical outcomes for precision agriculture.
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".