Interplay of physical mechanisms and biofilm processes: review of microfluidic methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bacteria in natural and artificial environments often reside in self-organized, integrated communities known as biofilms. Biofilms are highly structured entities consisting of bacterial cells embedded in a matrix of self-produced extracellular polymeric substances (EPS). The EPS matrix acts like a biological 'glue' enabling microbes to adhere to and colonize a wide range of surfaces. Once integrated into biofilms, bacterial cells can withstand various forms of stress such as antibiotics, hydrodynamic shear and other environmental challenges. Because of this, biofilms of pathogenic bacteria can be a significant health hazard often leading to recurrent infections. Biofilms can also lead to clogging and material degradation; on the other hand they are an integral part of various environmental processes such as carbon sequestration and nitrogen cycles. There are several determinants of biofilm morphology and dynamics, including the genotypic and phenotypic states of constituent cells and various environmental conditions. Here, we present an overview of the role of relevant physical processes in biofilm formation, including propulsion mechanisms, hydrodynamic effects, and transport of quorum sensing signals. We also provide a survey of microfluidic techniques utilized to unravel the associated physical mechanisms. Further, we discuss the future research areas for exploring new ways to extend the scope of the microfluidic approach in biofilm studies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it