Spectrophotometric-Based Assay to Quantify Relative Enzyme-Mediated Degradation of Commercially Available Bioplastics
Bibliographic record
Abstract
We present a spectrophotometric-based assay to identify enzymes that degrade commercially available bioplastics. Bioplastics comprise aliphatic polyesters with hydrolysis-susceptible ester bonds and are proposed as a replacement for petroleum-based plastics that accumulate in the environment. Unfortunately, many bioplastics can also persist in environments including seawater and waste centers. Our assay involves an overnight incubation of candidate enzyme(s) with plastic, followed by A610 spectrophotometry using 96-well plates to quantify both a reduction in residual plastic and the liberation of degradation by-products. We use the assay to show that Proteinase K and PLA depolymerase, two enzymes that were previously shown to degrade pure polylactic acid plastic, promote a 20-30% breakdown of commercial bioplastic during overnight incubation. We validate our assay and confirm the degradation potential of these enzymes with commercial bioplastic using established mass-loss and scanning electron microscopy methods. We show how the assay can be used to optimize parameters (temperature, co-factors, etc.) to enhance the enzyme-mediated degradation of bioplastics. The assay endpoint products can be coupled with nuclear magnetic resonance (NMR) or other analytical methods to infer the mode of enzymatic activity. Overall, the screening capacity of the spectrophotometric-based assay was demonstrated to be an accurate method to identify bioplastic-degrading enzymes.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".