Keywords
mass
USB memory
relativity
simulation
How to Cite
Abstract
This study examines the relationship between the energy stored and the mass change in digital storage devices, specifically USB flash drives, using Einstein's equation E = mc².
Although stored data do not have significant weight, they could theoretically cause an extremely small change in mass due to the energy involved in storing information. A mathematical model is proposed to calculate the energy consumed and the associated mass change when storing data.
Through MATLAB simulations, the theoretical model is validated, showing that the mass change is extremely small (in the femtogram range) and not measurable with current technology. The results emphasize the relevance of relativity in nano and quantum-scale systems, suggesting future applications in nanotechnology, quantum computing, and high-precision metrology.
While the practical impact is negligible with current technology, the theoretical results could be significant in the design of nanometer-scale energy storage systems.
References
Bennett, C. H. (1982). The thermodynamics of computation—A review. International Journal of Theoretical Physics, 21(12), 905–940. https://doi.org/10.1007/BF02084158
Bérut, A., Arakelyan, A., Petrosyan, A., Ciliberto, S., Dillenschneider, R. y Lutz, E. (2012). Experimental verification of Landauer’s principle linking information and thermodynamics. Nature, 483(7389), 187–189. https://doi.org/10.1038/nature10872
Chen, L., Liu, M. y Wang, Y. (2024). Energy impact of machine learning algorithms on hardware: A simulation study. Journal of Computer Science and Technology, 39(2), 245–260.
Cohen, E. R., Cvitanović, P., y Taylor, B. N. (2019). The 2018 CODATA recommended values of the fundamental physical constants. Reviews of Modern Physics, 91(3), 030001. https://doi.org/10.1103/RevModPhys.91.030001
Einstein, A. (1905). Zur Elektrodynamik bewegter Körper. Annalen der Physik, 17(10), 891–921. https://doi.org/10.1002/andp.19053221004
Einstein, A. (1915). Die Grundlage der allgemeinen Relativitätstheorie. Annalen der Physik, 49(7), 769–822. https://doi.org/10.1002/andp.19163540702
Feynman, R. P., Leighton, R. B., y Sands, M. (1965). The Feynman lectures on physics: Vol. I. Addison-Wesley.
Gilat, A. (2011). MATLAB: An introduction with applications. Wiley.
Gupta, P., y Sharma, R. (2023). Thermal modeling of integrated circuits using MATLAB simulations. IEEE Transactions on Circuits and Systems II: Express Briefs, 70(8), 1234–1240.
Jensen, K., Bhattacharya, M., y Datta, A. (2024). Quantum mass sensing with nanomechanical resonators. Nature Physics, 20(4), 567–573.
Kim, J., Park, H., y Lee, S. (2023). Energy-efficient memory architectures for AI workloads. IEEE Transactions on Computers, 72(3), 789–801.
Landauer, R. (1961). Irreversibility and heat generation in the computing process. IBM Journal of Research and Development, 5(3), 183–191. https://doi.org/10.1147/rd.53.0183
Lloyd, S., Chen, Y., y Zhang, J. (2022). Thermodynamic limits of quantum computing. Physical Review Letters, 129(15), 150601.
Nakamura, K., Tanabashi, M. y Particle Data Group. (2020). Review of particle physics. Physical Review D, 101(3), 030001. https://doi.org/10.1103/PhysRevD.101.030001
Shim, W., Jiang, H., Peng, X. y Yu, S. (2020). Architectural design of 3D NAND flash based compute-in-memory for inference engine. In MEMSYS 2020 Conference Proceedings (pp. 64–72). https://doi.org/10.1145/3422575.3422780
Taylor, E. F., y Wheeler, J. A. (1992). Spacetime physics: Introduction to special relativity. W.H. Freeman and Company.
Vopson, M. M. (2021). The mass-energy-information equivalence principle. AIP Advances, 11(12), 125206. https://doi.org/10.1063/5.0087175
Zhang, N., y Wu, F. (2024). Automobile-demand forecasting based on trend extrapolation and causality analysis. Electronics, 13(16), 3294. https://doi.org/10.3390/electronics13163294
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