Elucidation of Functional Groups on Gram-Positive and Gram-Negative Bacterial Surfaces Using Infrared Spectroscopy
Bibliographic record
Abstract
Surface functional group chemistry of intact Gram-positive and Gram-negative bacterial cells and their isolated cell walls was examined as a function of pH, growth phase, and growth media (for intact cells only) using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy. Infrared spectra of aqueous model organic molecules, representatives of the common functional groups found in bacterial cell walls (i.e., hydroxyl, carboxyl, phosphoryl, and amide groups), were also examined in order to assist the interpretation of the infrared spectra of bacterial samples. The surface sensitivity of the ATR-FTIR spectroscopic technique was evaluated using diatom cells, which possess a several-nanometers-thick layer of glycoprotein on their silica shells. The ATR-FTIR spectra of bacterial surfaces exhibit carboxyl, amide, phosphate, and carbohydrate related features, and these are identical for both Gram-positive and Gram-negative cells. These results provide direct evidence to the previously held conviction that the negative charge of bacterial surfaces is derived from the deprotonation of both carboxylates and phosphates. Variation in solution pH has only a minor effect on the secondary structure of the cell wall proteins. The cell surface functional group chemistry is altered neither by the growth phase nor by the growth medium of bacteria. This study reveals the universality of the functional group chemistry of bacterial cell surfaces.
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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".