Two-dimensional (2D) material nanofiltration membranes for effective recovery of lithium
Bibliographic record
Abstract
With the consistent increase in global demand for renewable energy, microelectronics, and electric vehicles, the demand for lithium has surged drastically in recent years to ensure sustainable growth of respective sectors. Recovery of lithium particularly from seawater has emerged as a cutting-edge technology to strengthen lithium resources. Since the innovation of two-dimensional (2D) materials, 2D materials-driven nanofiltration (NF) membranes have been on the top priority for lithium recovery, mainly due to their cost-effectiveness and energy efficiency. The most phenomenal aspect associated with 2D materials nanofiltration process is that exceptional ions and water permeation phenomena have been attained. These results are achieved mainly due to the existence of a synergistic effect between controlled pore size (stacking space available between adjacent layers) and surface properties of nanopores/nanochannels developed in membranes. In this review report, we have outlined and discussed various 2D materials including graphene, graphene oxide (GO), MXene (Ti 3 C 2 X), hexagonal-boron nitride (h-BN), metal–organic framework, metal covalent framework, and transition metal dichalcogenides (TMDs) deployed for construction of nanofiltration membranes along with their attained monovalent metal ions rejection outcomes, Li + ions in particular. Various strategies (i.e., defect engineering, cation regulations, and modification of surface functional groups) have been explained in detail in order to create nanopores into nanosheets and to tune the interlayer spacing of 2D nanofiltration membranes. Moreover, 2D materials composite nanofiltration membranes with improved metal ion rejection rates, hydrophobicity, enhanced structural integrity in varied pH solutions, and non-swelling characteristics have also been discussed. Finally, to promote the development of 2D materials-driven nanofiltration membranes with further enhanced lithium-ion recovery rates, rational design of membrane structures, relevant challenges, and future perspectives are insightfully addressed.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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".