Nanoarchitecture of Novel 3D Ion Transferring Channel Containing Composite Solid Polymer Electrolyte Membrane Based on Holey Graphene Oxide and Chitosan Biopolymer
Bibliographic record
Abstract
In three-dimensional (3D) nanoarchitecture arena, two-dimensional (2D) nanostructured graphene oxide (GO) and its derivatives have been emerged as a promising choice of advanced additive materials due to their outstanding properties: high specific surface area (2630 m2g-1), flexibility, light weight, diverse functionality, super mechanical and thermal stability. Particularly, holey graphene oxide (HGO)—single atom thick unique 2D porous nanosheet is obtained from exfoliation of functional GO—a class of graphene sheet with abundant nanopore in their plane; can potentially be used for designing, fabricating and evaluating advanced 3D nanocomposite materials for the development of demandable renewable energy technologies including next generation of solid-state Li+ and Na+ rechargeable batteries. Having been the most significant, momentous, and effectual commercial energy storage devices over the past decade, however, lithium-ion batteries (LIBs) have some key limitations: flammability, poor thermal, and mechanical stability. To overcome these existing limitations, incorporation of ceramic or polymer-based solid-state electrolytes into the LIBs are being explosively focused by the energy storage research community because of their full solid-state condition and tunable molecular level engineering scope. In this research work, a novel 3D nanocomposite solid polymer electrolyte membrane (SPEM) has been successfully developed based on 2D-HGO and chitosan (CH) biopolymer—naturally occurring only alkaline polysaccharide obtained from industrial shrimp shells, super cheap, nanostructured, non-toxic, completely biodegradable, also abundant in nature. In addition, a facial and cost-effective solution-casting technique has been utilized to fabricate SPEM and applied as a solid-state polymer electrolyte for next generation of flexible and wearable rechargeable LIB technology. To investigate the structural, morphological, thermal, mechanical, and electrochemical performance of as prepared SPEM, so far, a comprehensive characterization has been done with the help of SEM, TEM, XRD, TGA, DSC, FTIR, Raman, elemental analysis, tensile strength test, and electrochemical impedance spectroscopy techniques. The SEM analysis of as-prepared flexible, wearable, free-standing, and super thin (~0.08 mm) SPEM depicts the coherently aligned 2D-HGO nanosheets formed a uniform and strong interconnecting 3D ion transfer channels with the host CH biopolymer. Moreover, 1wt% HGO, almost evenly distributed nanofiller SiO2 particles along with polyvinylpyrrolidone (PVP) polymer binder played an important role to generate better mechanical and electrochemical properties in SPEM. Thus, SPEM exhibited impressive ionic conductivity (6.44 x 10-3 Scm-1 at 23.1 oC and 1.02×10-2 Scm-1 at 70oC), a high level of tensile strength (5.87 MPa) as well as 672% and 93.7% increase of tensile strength than that of without HGO additive and GO based nanocomposite membranes respectively. In fact, very low activation energy (Ea = 0.08 eV) value shows the approval of easy lithium-ion diffusion capability in the SPEM system. In addition, fast ion transfer mechanism in SPEM has been investigated with an in-depth dielectric study that tells us the mainly hopping mechanism is dominating in the novel 3D architecture of SPEM. Besides, the obtained impressive electrochemical and mechanical properties of as-prepared SPEM could assist to understand the further fundamental aspects and impacts of SPEM in the application of LIB technology.
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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 | 0.000 |
| 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".