Nanoporous Electrodes By Laser-Induced Carbonization and Patterning of Polymer Resins for Flexible Energy Storage
Bibliographic record
Abstract
Improvements to miniaturized and flexible energy storage are required for a variety of current and next-generation applications ranging from wearable devices to smart credit cards. IR laser patterning of commercially available polymer substrates (ex. Kapton) has become a versatile platform for the rapid prototyping and final fabrication of electrodes for such devices.1 However, the carbonized structures formed, which have been termed “laser-induced graphene”, are largely macroporous and possess a low bulk density which currently limits the achievable energy density and flexibility of such thin film devices. In this work, we explore improved substrate materials, morphologies and device architectures that lead to more energy dense systems. Specifically, the development of composites based on the waste biomass-derived polymer polyfurfuryl alcohol (PFA) will be discussed. This resin-forming polymer is known to carbonize at high yield and form a microporous carbon upon high temperature thermal treatment. We demonstrate that this material can be carbonized by the CO2 laser but only in the presence of graphene oxide. Thermal treatment of resins with incorporated graphene oxide suggest a significant catalytic effect on the process of graphitization. Furthermore, when the film is made into a porous vs. non-porous resin, we observe no expansion or macropore development such that the electrodes remain embedded in the film – likely due to the short path length in the porous network for decomposition gases to escape. The improved electrode structure results in interdigitated electrodes achieving nearly five-fold increased areal capacitance and improved flexibility. In addition to supercapacitor development, we will also discuss our recent efforts to develop flexible batteries based on the laser-induced graphene approach. In particular, we demonstrate the ability to nucleate/grow sulfur onto laser-induced graphene electrodes which can be melt-imbibed into the microporous network. We also demonstrate the ability to electroplate lithium metal directly onto the laser-scribed anode using a combination of pulse-reverse-pulse deposition2 and Ag-nanoparticles as a nucleation aid.3 While promising for device applications, the resulting flexible Li-S battery system is also useful for in situ investigations of speciation during battery operation. References: Lin, J., Peng, Z., Liu, Y., Ruiz-Zepeda, F., Ye, R., Samuel, E.L., Yacaman, M.J., Yakobson, B.I. and Tour, J.M., 2014. Laser-induced porous graphene films from commercial polymers. Nature communications, 5, p.5714. Yang, H., Fey, E.O., Trimm, B.D., Dimitrov, N. and Whittingham, M.S., 2014. Effects of Pulse Plating on lithium electrodeposition, morphology and cycling efficiency. Journal of Power Sources, 272, pp.900-908. Yan, K., Lu, Z., Lee, H.W., Xiong, F., Hsu, P.C., Li, Y., Zhao, J., Chu, S. and Cui, Y., 2016. Selective deposition and stable encapsulation of lithium through heterogeneous seeded growth. Nature Energy, 1, p.16010. Figure 1
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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 | 0.000 | 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".