Biofabrication, spectroscopic, and photocatalytic studies of titania nanoparticles mediated by <i>Proteus mirabilis</i> strain NG-ABK-32 for smart applications
Bibliographic record
Abstract
Green fabrication of nanoparticles (NPs) is simple, pocket-friendly, easy, sustainable, and devoid of eco-toxicity issues. Thirty-five bacterial strains were isolated from ‘tie and dye’ effluent-contaminated soil and effluent samples and were identified using traditional methods. Following a dye decolourization assay, Proteus mirabilis strain (NG-ABK-32) emerged as the isolate with the highest potential to decolourize vat violet RR dye. Titania nanoparticles (TiO2 NPs) fabricated using NG-ABK-32 strain were characterized by UV-visible spectroscopy (UV-vis), Transmission Electron Microscopy (TEM), X-ray Diffraction (XRD), and Fourier Transform Infrared (FT-IR) spectroscopy. UV-Vis result displayed an absorption peak at 320 nm. The TEM image revealed monodispersed, spherical NPs possessing an average particle size of 3.18 nm. The crystallographic plane of rutile TiO2 NPs and high purity were confirmed by the XRD pattern. The average crystallite size was 52.53 nm. The synthesis of TiO2 NPs was further established from FT-IR by the presence of Ti-O, Ti-OH, and O-Ti-O functional groups. The synergistic treatment caused a significant reduction of total suspended solids (TSS), total dissolved solids (TDS), biological oxygen demand (BOD), chemical oxygen demand (COD), and color (Pt.Co) of the effluent by 96.86%, 78.78%, 67.84%, 56.65%, 67.84%, and 72.09% respectively. Thus, this study confirmed the better efficiency of combining biological (NG-ABK-32) pre-treatment with photocatalytic degradation post-treatment using the synthesized TiO2 NPs in the treatment of ‘tie and dye’ effluent compared to the application of the TiO2 NPs as a single treatment method.
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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".