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ZENODO
Model . 2025
Data sources: ZENODO
ZENODO
Model . 2025
Data sources: Datacite
ZENODO
Model . 2025
Data sources: Datacite
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E-Battery

Authors: Stone, Travis Raymond-Charlie;
Abstract

E Battery Architect Travis Raymond-Charlie Stone Assistant AI: Perplexity AI Report: Estimating Physical Scale of Transistor-Based Energy Micro-Reservoirs for Bulk Storage Abstract This report explores the feasibility and physical scale requirements for implementing a transistor-based micro-reservoir energy storage system capable of bulk energy storage comparable to conventional laptop batteries. By considering the inherent energy storage capacity of transistor parasitic capacitances and leveraging recent advancements in nanoscale 3D printing, the report estimates transistor counts and corresponding volumes necessary for practical applications. The findings demonstrate that while planar transistor storage at conventional semiconductor densities would be prohibitively large, advanced 3D printing techniques could enable dense three-dimensional integration, potentially reducing physical size to a manageable scale for consumer electronics. Introduction Recent concepts propose using transistor logic circuits and parasitic capacitances as controllable micro-reservoirs for energy storage within electronic systems. While transistor gates inherently store extremely small amounts of electric energy (on the order of picojoules per element), combining many such elements in series and parallel networks offers a theoretical pathway to increase total stored charge. This study quantifies the physical scale of transistor arrays necessary to achieve energy storage comparable to the typical 40-60 Wh capacity found in laptop batteries, evaluates the size challenges in conventional fabrication methods, and investigates the potential impact of advanced 3D nanofabrication. Methods Energy per transistor estimated as ~1 picojoule (10⁻¹² J) based on parasitic capacitance values. Target bulk energy storage set equivalent to a 40 Wh laptop battery (~144,000 joules). Transistor counts calculated by dividing total target energy by energy per transistor. Conventional 2D planar chip transistor density approximated at 1.7×1081.7×108 transistors/mm² for 5 nm process technology. Area and volume for planar and 3D stacked configurations were estimated using standard geometric formulas. 3D printing feature size assumed at ~100 nm per transistor to model potential ultradense volumetric integration. Results Number of transistors required for 40 Wh energy storage:1.44×105J1×10−12J=1.44×10171×10−12J1.44×105J=1.44×1017 transistors. Area for planar layout at 5 nm node:1.44×10171.7×108=5881.7×1081.44×1017=588 m² (approx. 3-4 tennis courts). Cube side length assuming planar area laid flat: ~24 m; stacked to cube: ~8.3 m per side. Using 3D nanoscale printing (~100 nm³ per transistor), volume reduces to ~100 cm³ cube (~4.6 cm per side). Discussion Traditional planar semiconductor fabrication cannot accommodate the transistor counts needed for bulk energy storage within practical size constraints, emphasizing the limitations of parasitic capacitance-based storage at scale. However, emerging nanoscale 3D printing techniques, such as two-photon polymerization, potentially enable ultradense transistor stacking orders of magnitude smaller in volume while preserving transistor functionality. This reconceptualization could lead to viable transistor-based micro-reservoir energy storage devices for integration into consumer electronics, aiding in energy management and lifespan extension. Conclusion While transistor-based micro energy storage at current planar densities is impractically large for bulk applications, incorporation of advanced 3D printing and materials science dramatically reduces physical size requirements, bringing concept feasibility closer to reality. Combining these micro-reservoir arrays with traditional battery technology presents an exciting hybrid approach to next-generation energy storage and management in portable electronics. https://sib.illinois.edu/undergraduate/guidelines/manuscript https://www.nature.com/srep/author-instructions/submission-guidelines https://www.bates.edu/biology/files/2010/06/How-to-Write-Guide-v10-2014.pdf https://libguides.westminster.ac.uk/report-writing/scientific-reports https://writing.wisc.edu/handbook/sciencereport/ https://www.epfl.ch/labs/gem/wp-content/uploads/2018/07/Guidelines-for-writing-a-scientific-report.pdf https://www.ncl.ac.uk/academic-skills-kit/assessment/assignment-types/structuring-a-science-report/ https://www.grammarly.com/blog/academic-writing/how-to-write-a-report/ https://pmc.ncbi.nlm.nih.gov/articles/PMC4975199/ https://guides.lib.uci.edu/c.php?g=334338&p=2249906

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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