ORIGINAL RESEARCH: Centrifugal recovery of rhizobial cells from fermented starch industry wastewater & development of stable formulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Application of rhizobial inoculant is an agricultural practice successfully used to increase growth and yield of leguminous plants. Development of a concentrated culture is essential for advanced formulations. This study assessed the use of starch industry wastewater as a potent carbon source for production of Sinorhizobium meliloti, with a maximum cell count of 4×109 CFU/mL. Optimal conditions for maximum recovery of cells (>99%) during centrifugation were determined to be 8 000 g, at a temperature of 20 °C for 20 min, pH 7, using a swinging bucket rotor and a surface response methodology. Of the different centrifugal aids tested, starch (2% w/v) best minimized the loss of microbial cells during recovery from the supernatant. A fixed- angle-rotor experiment was also carried out to determine differences in recovery between centrifugal configurations; optimal recovery was observed with the swinging bucket rotor (>99%) versus a fixed angle rotor (90%). A further decrease (from 90% to 87%) in recovery was observed with a 20 times increase in broth volume at 8 000 g, centrifuged for 20 min at 20 °C. The addition of soya oil during centrifugation contributed to emulsion formulation. A slight decrease was observed in CFU values using an antimicrobial agent, as compared to the control of centrifugate and oil emulsified with 0.1% v/v surfactant, after one month of storage. Suspension formulation with alginate additive showed a cell viability of more than 10 9 CFU/mL after 9 weeks of storage. This study demonstrates the feasibility of cell recovery and simultaneous formulation development of Sinorhizobium. Further investigation of parameter optimization for development of advanced formulations using recovered cells is warranted.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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 it