From Coated to Uncoated: Scanning Electron Microscopy Corrections to Estimate the True Surface Pore Size in Nanoporous Membranes
Bibliographic record
Abstract
Scanning electron microscopy (SEM) is the premier method for characterizing the nanoscale surface pores in ultrafiltration (UF) membranes and the support layers of reverse osmosis (RO) membranes. Based on SEM, the conventional understanding is that membranes typically have low surface porosities of <10%. We demonstrated and quantified how the high acceleration voltage during SEM imaging and the sputtered-metal coating thickness required for SEM systematically underestimate membrane surface porosity and pore size. We showed that imaging a commercial UF membrane at 1, 5, and 10 kV reduced the measured surface porosity from 10.3 ± 0.3% (1 kV) to 6.3 ± 0.4% (10 kV), while increasing the Pt coating thickness from 1.5 to 5 nm reduced the porosity by 54% for the UF membrane (12.9 ± 0.9% to 5.8 ± 0.6%) and 46% for an RO support (13.1 ± 0.6% to 7.0 ± 0.2%). To account for the coating thickness, we then developed a digital correction method that simulates pore dilation, enabling the surface pore structure to be estimated for uncoated membranes. Pore dilation yielded uncoated surface porosity values of 23% for the UF membrane and 20% for the RO support, which are approximately 3-fold greater than the directly observed values for a typical coating thickness of 4 nm. Similarly, mean pore diameters for uncoated membranes were 2-fold greater for the UF membrane and 1.5-fold greater for the RO support than directly observed. Critically, the dilation-derived pore-size distributions agreed with low-flux dextran-retention measurements fitted with the Bungay-Brenner model. Our results suggest that the surface porosities and pore sizes of nanoporous membranes are much larger than previously understood, which has major implications for structure/transport relationships. For future nanoscale pore analysis of membranes (and other nanoporous materials), we recommend low acceleration voltage (1 kV), minimal coatings (1-2 nm), and digital dilation to account for coating-induced artifacts.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".