Growth of Escherichia coli on the GaAs (001) surface
Bibliographic record
Abstract
Detection of pathogenic bacteria and monitoring their susceptibility to antibiotics are of great importance in the fields of medicine, pharmaceutical research, as well as water and food industries. In order to develop a photonic biosensor for detection of bacteria by taking advantage of photoluminescence (PL) of GaAs-based devices, we have investigated the capture and growth of Escherichia coli K12 on bare and biofunctionalized surfaces of GaAs (001) – a material of interest for capping different semiconductor microstructures. The results were compared with the capture and growth of Escherichia coli K12 on Au surfaces that have commonly been applied for studying a variety of biological and biochemical reactions. We found that neither GaAs nor Au-coated glass wafers placed in Petri dishes inoculated with bacteria inhibited bacterial growth in nutrient agar, regardless of the wafers being bare or biofunctionalized. However, the capture and growth of bacteria on biofunctionalized surfaces of GaAs and Au wafers kept in a flow cell and exposed to different concentrations of bacteria and growth medium revealed that the initial surface coverage and the subsequent bacterial growth were dependent on the biofunctionalization architecture, with antibody-coated surfaces clearly being most efficient in capturing bacteria and offering better conditions for growth of bacteria. We have observed that, as long as the GaAs wafers were exposed to bacterial suspensions at concentrations of at least 105 CFU/mL, bacteria could grow on the surface of wafers, regardless of the type of biofunctionalization architecture used to capture the bacteria. These results provide important insight towards the successful development of GaAs-based devices designed for photonic monitoring of bacterial reactions to different biochemical environments.
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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".