(Invited) Nanotechnology for Industrial Electrochemical Energy Storage
Bibliographic record
Abstract
Nanomaterials are expected to play important roles in the realization of our future society, expected to contribute to the realization of a sustainable society through ensuring smart materials to enable sustainability of water, air, and materials; and energy materials and devices to enable renewable energy production and energy saving. These have enormous industrial/commercial ramifications. Our society needs to mitigate or ideally reverse global climate change caused by anthropogenetic CO2 emissions resulting from our fossil-fuel-fed society. Electrification, which encourages and enables a circular society is an important part of the solution. It leads to urgent needs for new nanotechnologies and nanomaterials for improved solar cells, batteries, electrolyzers, and the fuel cell components that are needed to support a hydrogen economy. As a prime example, Li-ion batteries, for which the Nobel Prize was awarded in 2019, are the most popular rechargeable batteries today and have become the main power source not only for everyday needs such as portable electronic devices but also for larger-scale applications that will become indispensable society in the very near future. Although enormous effort has been devoted to improving the electrochemical performance of a large number of Li-based materials, today’s rechargeable batteries have energy densities that remain below theoretical values, and still have far-from-optimal longevity and safety. None of the current rechargeable batteries can meet all the challenging requirements for our energy-storage needs, so the race is on to develop next-generation Li-based systems that encapsulate the desired characteristics of high energy density, low cost, and improved safety. Success in this arena would have tremendous impact on a wide range of technologies ranging from EVs (land and marine), to drones, airplanes, robots, and grid-scale energy storage. The use of nanotechnology─which has progressed tremendously and continues to establish new ground─is vital to address the significant challenges that next-generation batteries present. These materials challenges remain whether the batteries operate on the basis of typical intercalation chemistry or conversion chemistry. Some of these challenges -as will be covered in this presentation - lie in the development of (a) nanomaterials that can withstand significant volume changes and bond rearrangements during conversion redox reactions, (b) nanocoatings that form protective layers on either the positive or negative electrode to stabilize the electrode–electrolyte interface at either high or low voltage, and (c) nanotechnologies to engineer solid–solid interfaces in all-solid-state batteries. Nanotechnology enables materials scientists to bring novel functions to all battery components that cannot be achieved by conventional approaches. This topic will be the central focus of this presentation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.043 |
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".