Automated Ultrafiltration Device for Efficient Collection of Environmental Nanoparticles from Aqueous Suspensions
Bibliographic record
Abstract
Environmental nanoparticles exist in the hydrosphere, pedosphere, biosphere, and atmosphere. Their biogeochemical and ecological impacts are some of the fastest growing areas of research today. However, efficient separation of environmental nanoparticles remains difficult. The objective of this study was to develop an automated ultrafiltration device (AUD) for efficient collection of environmental nanoparticles. The AUD utilizes an automated hydraulic ram to facilitate collection of nanoparticles using the ultrafiltration membrane with pore size in the range of 1 to 100 nm. Zeolite A was used as a model nanoparticle sample to demonstrate the efficiency of the AUD. The size distribution and mean particle sizes determined by zeta‐sizer analysis on the collected nanoparticles and their transmission electron micrographs indicated the adequacy of the AUD developed in this study in collecting nanoparticles (1–100 nm). Because of its ability to reduce the time needed for sample collection, coupled with the quantity of nanoparticles collected, the AUD was far more efficient than the conventional syringe method for collecting nanoparticles. The AUD has the characteristics of automation, easy operation, and high efficiency in the separation of nanoparticles and would, thus, facilitate future research and developments in environmental nanoscience and nanotechnology and their impacts on the ecosystem.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".