Life Cycle Assessment of Electrospun Cellulose-Based NanocompositeMembrane Fabrication
Bibliographic record
Abstract
Polymer nanocomposite membrane is an innovative and promising approach with a broad spectrum of potential applications in filtration processes.It is used to selectively separate molecules and ions.A comprehensive understanding of its environmental impacts, covering the life cycle of the used materials and the fabrication process, is crucial for its long-term sustainable success.This research aims to elaborate and implement a decision-making tool for greener membrane fabrication process.The environmental impacts of synthesizing one batch of Nanocomposite cellulose nanofibrils/cellulose acetate membrane using 50 gr polymer dope solution by electrospinning technique was determined based on a life cycle assessment methodology.The eco-sufficiency and sustainability of the electrospinning method were evaluated through a cradle-to-gate life cycle assessment (LCA) adopting the Cumulative Energy Demand (CED), and IMPACT2002+ impact assessment methods.According to CED assessment, the majority of energy consumed during electrospun membrane synthesis, amounting to 382 MJ, was consumed by the production of cellulose nanofibers.This is related to nonrenewable fossil energy consumed in Ethanol production.As per IMPACT2002+ impact assessment, cellulose acetate and cellulose nanofiber manufacturing, and medium voltage electricity are the main contributors to the overall midpoint environmental effects.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".