Comparison of supported liquid membranes and solid-phase extraction for quantitative removal of lead from aqueous solutions
Bibliographic record
Abstract
The selective transport of a target metal ion across a membrane from a donor to an acceptor solution is an attractive concept for preconcentration or separation in a flow injection (FI) system. However, we found that the transport of lead across a supported liquid membrane consisting of dicyclohexano-18-crown-6 in decanol embedded in Celgard 2400 was too slow to be practical for an FI atomic spectrometry system. On the other hand, the same chemistry in an on-line, solid-phase reactor (a mini-column containing Pb-Spec resin) gave a satisfactory performance. In an attempt to understand the kinetics of the system, we have calculated the rate of transport across the membrane. As flux is proportional to concentration on the donor side which decreases with time we performed a series of numerical simulations involving fixed time intervals of decreasing values. These calculations show that for the sizes of membranes in a typical FI system, transport is very slow compared with the adsorption of lead by the Pb-Spec resin: it takes about 100 min for the concentration in a 0.06 mL donor solution to decrease by a factor of 1000. The time required for the same decrease in concentration for a membrane having the same surface area and ligand concentration as the Pb-Spec column was calculated as approximately 9 s.Key words: supported liquid membrane, solid-phase extraction, flow-injection, lead.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".