Combatting biofilms in potable water systems: A comprehensive overview to ensuring industrial water safety
Bibliographic record
Abstract
Biofilm formation in industrial potable water systems, encompassing applications such as drinking, emergency showers, firefighting and sanitary appliances, presents a multifaceted challenge that has significant implications for both equipment efficiency and human health. These microbial communities, comprised of bacteria, fungi and protozoa, adhere to surfaces and are embedded within an extracellular matrix, primarily of polysaccharide origin. The formation and persistence of these biofilms can lead to reduced system efficiency and potential health risks due to microbial-induced corrosion, contamination and waterborne pathogens. This review delves into the physicochemical and microbial factors promoting biofilm growth in these systems and elucidates contemporary strategies for their control and eradication. By harnessing advanced methodologies, including state-of-the-art filtration, disinfection techniques and predictive monitoring, stakeholders can proactively address biofilm-related challenges. The emphasis of this comprehensive overview is on the interdisciplinary nature of biofilm growth, combining insights from microbiology, engineering and water chemistry to pave the way for an integrative approach to ensuring consistent industrial water quality.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".