Quick Navigation
Topics
Benchmarking Verification Validation
Quantum Sensing Metrology
Quantum Software Tools Programming
Lambert W Function Framework for Graphene Nanoribbon Quantum Sensing: Theory, Verification, and Multi-Modal Applications
arXiv
Authors: F. A. Chishtie, K. Roberts, N. Jisrawi, S. R. Valluri, A. Soni, P. C. Deshmukh
Year
2026
Paper ID
3784
Status
Preprint
Abstract Read
~2 min
Abstract Words
160
Citations
N/A
Abstract
We establish a rigorous mathematical framework connecting graphene nanoribbon quantum sensing to the Lambert W function through the finite square well (FSW) analogy. The Lambert W function, defined as the inverse of f(W) = WeW, provides exact analytical solutions to transcendental equations governing quantum confinement. We demonstrate that operating near the branch point at z = -1/e yields sensitivity enhancement factors scaling as ηenh propto \(z - zc\)-1/2, achieving 35-fold enhancement at δ= 0.001. Comprehensive numerical verification confirms: (i) all seven bound states for strength parameter R = 10 satisfying the constraint u2 + v2 = R2; (ii) exact agreement between theoretical band gap formula Eg = 2πhbar vF/(3L) and empirical relation Eg = 1.38/L eVcdotnm; (iii) universal sensitivity scaling across biomedical (SARS-CoV-2, inflammatory markers, cancer biomarkers), environmental CO$2$, CH$4$, NO$2$, N$2$O, H$2$O, and physical (strain, magnetic field, temperature) sensing modalities. This unified framework provides design principles for next-generation graphene quantum sensors with analytically predictable performance.
Why This Paper Matters
- This paper contributes to the Quantum Sensing & Metrology research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- We establish a rigorous mathematical framework connecting graphene nanoribbon quantum sensing to the Lambert W function through the finite square well (FSW) analogy.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.