Application of Time-of-Flight-Secondary Ion Mass Spectrometry for the Detection of Enzyme Activity on Solid Wood Substrates
Bibliographic record
Abstract
Time-of-flight-secondary ion mass spectrometry (TOF-SIMS) is a surface analysis technique that is herein demonstrated to be a viable tool for the detection of enzyme activity on solid substrates. Proof-of-principle experiments are presented that utilize commercial cellulase and laccase enzymes, which are known to modify major polymeric components of wood (i.e., cellulose and lignin, respectively). Enzyme activity is assessed through principle component analysis (PCA) as well as through peak ratios intended to measure selective enzymatic wood degradation. Spectral reproducibility of the complex wood substrates is found to be within 5% relative standard deviation (RSD), allowing for relative quantification of changes in wood composition. Procedures are also presented to identify and avoid the influence of mass interferences from protein adsorption by the enzyme solutions. The activity of a cellulase cocktail is clearly evident through the TOF-SIMS spectra and is supported by high-pressure liquid chromatography (HPLC) measurements of sugar release and by complementary X-ray photoelectron spectroscopy (XPS) measurements of the wood surfaces. Laccase activity, which is mediated through small organic molecules, can be detected in the TOF-SIMS spectra through a decrease in G and S lignin peaks. This work has positive implications for the development of qualitative, high-throughput screening assays for enzyme activity on industrially relevant, lignocellulosic substrates.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".