Fully 3D-Printed Preconcentrator for Selective Extraction of Trace Elements in Seawater
Bibliographic record
Abstract
In this study, we used a stereolithographic 3D printing technique and polyacrylate polymers to manufacture a solid phase extraction preconcentrator for the selective extraction of trace elements and the removal of unwanted salt matrices, enabling accurate and rapid analyses of trace elements in seawater samples when combined with a quadrupole-based inductively coupled plasma mass spectrometer. To maximize the extraction efficiency, we evaluated the effect of filling the extraction channel with ordered cuboids to improve liquid mixing. Upon automation of the system and optimization of the method, the device allowed highly sensitive and interference-free determination of Mn, Ni, Zn, Cu, Cd, and Pb, with detection limits comparable with those of most conventional methods. The system's analytical reliability was further confirmed through analyses of reference materials and spike analyses of real seawater samples. This study suggests that 3D printing can be a powerful tool for building multilayer fluidic manipulation devices, simplifying the construction of complex experimental components, and facilitating the operation of sophisticated analytical procedures for most sample pretreatment applications.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".