Metal Organic Framework Derived Nanomaterials in the Application of Lithium-Ion and Sodium-Ion Battery
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) have become one of the most widely used powers for portable electronic devices such as laptops, mobile phones, medical microelectronic devices and electrical vehicles with many outstanding features. Recently, the rareness and uneven distribution of lithium become a serious challenge for large scale application, which also resulting the high cost of LIBs. As the alternative for LIBs, sodium-ion battery (SIBs) has attracted increasing attention for the clean energy storage device due to the low cost as well as the high abundance than lithium [1]. However, the larger radius of Na ion will lead to the large volume exchange and sluggish kinetics, resulting the unstable of the electrode, especially for anode materials. To date, many efforts are attempted to manufacture different anode materials including novel carbon, metal oxide and metal sulfide for SIBs. Metal organic frameworks (MOFs), as a new class of porous crystalline materials, have been gradually studied as the precursors and templates for the design of porous carbon, metal oxide, metal sulfide and hierarchical nanostructure in the application of clean energy, such as batteries, fuel cells and supercapacitors [2]. In this case, we have developed different types of porous carbon and heteroatom (N, S) doped porous carbon derived from MOFs with high surface area and porosity. The N, S co-doped porous carbon delivers a highest capacity over 370 mAh g-1 at the current density of 50 mAg-1 and excellent rate capability with 196.5 mAh g-1 at 2000 mAg-1 in the application of SIBs. When test in LIBs, it also shows the high reversible capacity over 840 mAh g-1 at 100 mAg-1 after 100 cycles. Then, the porous SnO2 and Sn@carbon composites are achieved by design of new kinds of Sn-based MOFs. In Sn@C composites, Sn nanoparticles are surrounded by carbon matrix inheriting from MOFs with the improved performances than the porous SnO2 in both SIBs and LIBs. Furthermore, porous metal sulfide and 3D nanostructures based on MOFs also can be synthesized with the controllable morphologies and satisfactory performances. In conclusion, MOFs-derived nanomaterials show great potential in the application of LIBs and SIBs due to their unique properties, which lead to superior performance based on controllable shape, particle size, crystal structure, and purity based on the design of MOFs. Thus, MOFs and MOFs-based materials is a promising approach for the building of potential anode materials for high performances LIBs and SIBs. Reference [1] Karthikeyan Kaliyappan, Jian Liu, Andrew Lushington, Ruying Li, Xueliang Sun, Highly Stable Na2/3(Mn0.54Ni0.13Co0.13)O2 Cathode Modified by Atomic Layer Deposition for Sodium-Ion Batteries, ChemSusChem , 2015, 8, 2537 – 2543 [2] Yang Zhao, Zhongxin Song, Xia Li, Qian, Sun, Niancai Cheng, Stephen Lawes, Xueliang Sun, Metal Organic Frameworks for Energy Storage and Conversion, Energy Storage Materials , 2016, 2, 35–62
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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".