Bibliographic record
Abstract
The 21st century has been marked by an exponential increase in new and interconnected technologies and an awareness of the pressing need to decarbonize our energy supply. As a consequence, the demand for higher performing, versatile and environmentally benign energy storage has skyrocketed. Organic electrode materials are attractive candidates to address these needs because they are inexpensive, abundant, adaptable to different form factors, and their properties can be tuned at the molecular level by synthetic modification. Organic energy storage research has experienced a resurgence in the last decade, however many challenges must be overcome to achieve commercialization. In this thesis, I present the design of new organic polymers for energy storage systems that address specific challenges of organic electrodes. In Chapters 2 and 3, I leverage the advantageous properties of pyrene, namely surface area, electron transport and stability, to design conjugated polymers for supercapacitors and lithium-ion batteries. Chapter 2 explores a pyrene-fused thienopyrazine polymer as an n-type supercapacitor electrode. The extended conjugation afforded by pyrene results in good cycling stability relative to literature examples, and the material design provides a platform for further improvements in stability. In Chapter 3, I design a pyrene-fused azaacene polymer anode and investigate its mechanism of superlithiation and activation. I demonstrate the highest capacity for a linear polymer anode to date, attributed to high stability and extended conjugation. Analysis of the cycled electrodes indicates a deformation-based mechanism of activation, whereby cycling results in increased order and sp2 character within the electrode. Importantly, the electrodes maintain their high capacity across a ten-fold increase in rate, suggesting that high capacity superlithiation anodes will be achievable at practical rates. In Chapter 4, I describe synthetic strategies to develop norbornene-based pendant polymers for lithium-ion batteries. The chapter is divided into two sections, that each detail multiple generations of polymers that leverage the versatility of norbornene to achieve high capacity, high voltage or high rate capability. Synthetic design and future applications are discussed in detail. Lastly, Chapter 5 summarizes potential extensions of the above projects and provides an overview of the remaining challenges in the organic energy storage field.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".