The role of charge on the diffusion of solutes and nanoparticles (silicon nanocrystals, nTiO2, nAu) in a biofilm
Bibliographic record
Abstract
Environmental context The mobility and bioavailability of both contaminants and nutrients in the environment depends, to a large extent, on their diffusion. Because the majority of microorganisms in the environment are embedded in biofilms, it is essential to quantify diffusion in biofilms in order to evaluate the risk of emerging contaminants, including nanomaterials and charged solutes. This study quantifies diffusion, in a model environmental biofilm, for a number of model contaminants of variable size and charge. Abstract The effect of solute and biofilm charge on self-diffusion (Brownian motion) in biofilms is examined. Diffusion coefficients (D) of several model (fluorescent) solutes (rhodamine B; tetramethylrhodamine, methyl ester; Oregon Green 488 carboxylic acid, succinimidyl ester and Oregon Green 488 carboxylic acid) and nanoparticles (functionalised silicon, gold and titanium) were determined using fluorescence correlation spectroscopy (FCS). Somewhat surprisingly, little effect due to charge was observed on the diffusion measurements in the biofilms. Furthermore, the ratio of the diffusion coefficient in the biofilm with respect to that in water (Db/Dw) remained virtually constant across a wide range of ionic strengths (0.1–100 mM) for both negatively and positively charged probes. In contrast, the self-diffusion coefficients of nanoparticles with sizes >10 nm greatly decreased in the biofilms with respect to those in water. Furthermore, much larger nanoparticles (>66 nm) appeared to be completely excluded from the biofilms. The results indicated that for many oligotrophic biofilms in the environment, the diffusion of solutes and nanoparticles will be primarily controlled by obstruction rather than electrostatic interactions. The results also imply that most nanomaterials will become significantly less mobile and less bioavailable (to non-planktonic organisms) as they increase in size beyond ~10 nm.
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 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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".