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An Integrated Nanocomposite Proximity Sensor: Machine Learning-Based Optimization, Simulation, and Experiment paper by Independent / Community
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Paper

An Integrated Nanocomposite Proximity Sensor: Machine Learning-Based Optimization, Simulation, and Experiment

by Independent / Community 000e83ed6b57e93d6181c62b88062abca0a8aaa9
Free2AITools Nexus Index
65.4
S: Semantic 50

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A: Authority 75
P: Popularity 50
R: Recency 100
Q: Quality 65
Tech Context
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This paper utilizes multi-objective optimization for efficient fabrication of a novel Carbon Nanotube (CNT) based nanocomposite proximity sensor. A previously developed model is utilized to generate a large data set required for optimization which included dimensions of the film sensor, applied excitation frequency, medium permittivity, and resistivity of sensor dielectric, to maximize sensor sensitivity and minimize the cost of the material used. To decrease the runtime of the original model...

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Semantic Scholar 9 Citations
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Registry ID 000e83ed6b57e93d6181c62b88062abca0a8aaa9
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{000e83ed6b57e93d6181c62b88062abca0a8aaa9,
  author = {Unknown},
  title = {An Integrated Nanocomposite Proximity Sensor: Machine Learning-Based Optimization, Simulation, and Experiment Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/000e83ed6b57e93d6181c62b88062abca0a8aaa9}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). An Integrated Nanocomposite Proximity Sensor: Machine Learning-Based Optimization, Simulation, and Experiment [Paper]. Free2AITools. https://api.semanticscholar.org/000e83ed6b57e93d6181c62b88062abca0a8aaa9

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 75
Popularity (P) 50
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for An Integrated Nanocomposite Proximity Sensor: Machine Learning-Based Optimization, Simulation, and Experiment: Authority (A:75), Popularity (P:50), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"This paper utilizes multi-objective optimization for efficient fabrication of a novel Carbon Nanotube (CNT) based nanocomposite proximity sensor. A previously developed model is utilized to generate a large data set required for optimization which included dimensions of the film sensor, applied excitation frequency, medium permittivity, and resistivity of sensor dielectric, to maximize sensor sensitivity and minimize the cost of the material used. To decrease the runtime of the original model..."

❝ Cite Node 

@article{Unknown2026An,
  title={An Integrated Nanocomposite Proximity Sensor: Machine Learning-Based Optimization, Simulation, and Experiment},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“Š Research Signals

πŸ“ˆ9CitationsSemantic Scholar
πŸ›οΈ75AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
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source
semantic_scholar
author
Unknown
license
ArXiv
tags
paper, research, academic

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params billions
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