Recycling agro-industrial waste to produce amylase and characterizing amylase–gold nanoparticle composite
Bibliographic record
Abstract
Abstract Purpose Amylase being one of the most important industrial enzymes requires large-scale production. When producing an enzyme, high productivity, high purity and low production costs need to be considered. This study focuses on comparing various agro-industrial waste substrates, for production of alpha-amylase using Bacillus amyloliquefaciens. Moreover, it studies the stability and activity of amylase–gold nanoparticles composite. Methods This study is divided into two parts, in the first part various agro-industrial waste substrates, such as wheat bran, rice bran and potato peel were used to produce alpha-amylase using solid-state fermentation (SSF). The production of the enzyme was quantified and compared in specific enzyme activity units. In the second part, change in the stability and activity of amylase in enzyme–gold nanoparticles (AuNPs) composite has been discussed. Results Highest enzyme production was observed in wheat bran and potato peel substrate with specific enzyme activity of almost 1.2 U/ug and 1.1 U/ug. Among combination substrates, wheat bran with potato peel showed a high enzyme production of 1.3 U/ug. On the other hand, the optimum temperature for amylase activity shifted to 55 °C in the composite compared to 37 °C for free enzyme. Conclusions Comparison of specific enzyme activity of extracts from various substrates showed that wheat bran alone, and in combination with potato peel, produces active and pure amylases. To stress on various catalytic activities of alpha-amylase, the capability of the enzyme to synthesize gold nanoparticles and the effect of conjugation of the nanoparticle on its optimum catalytic activity are also discussed in this paper.
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 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".