“Biomass to Membrane”: Sulfonated Kraft Lignin/PCL Superhydrophilic Electrospun Membrane for Gravity-Driven Oil-in-Water Emulsion Separation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biobased membranes made with green solvents have numerous advantages in the water purification industry; however, their long-term use is impeded by severe membrane fouling and low structural stability. Herein, we proposed a facile and green approach to fabricate an eco-friendly and biodegradable electrospun membrane by simply blending polycaprolactone (PCL) with sulfonated kraft lignin (SKL) in a green solvent (i.e., acetic acid) without needing any additional post-treatment. We investigated the influence of SKL content on the surface morphology, chemical composition, and mechanical properties of the electrospun membrane. The SKL-modified membranes (L-5 and L-10) showed superhydrophilicity and underwater superoleophobicity with a water contact angle (WCA) of 0° (<3 s) and an underwater–oil contact angle (UWOCA) over 150° due to the combined effect of surface roughness and hydrophilic chemical functionality. Furthermore, the as-prepared membranes demonstrated excellent pure water flux of 800–900 LMH and an emulsion flux of 170–480 LMH during the gravity-driven filtration of three surfactant-stabilized oil-in-water emulsions, namely, mineral oil/water, gasoline/water, and n -hexadecane/water emulsions. In addition, these membranes exhibited superior antioil-fouling performance with excellent separation efficiency (97–99%) and a high flux recovery ratio (>98%). The 10 wt % SKL-incorporated membrane (L-10) also showed consistent separation performance after 10 cyclic tests, indicating its excellent reusability and recyclability. Furthermore, the stability of the membrane under harsh pH conditions was also evaluated and proved to be robust enough to maintain its wettability in a wide pH range (pH 1–10).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 it