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This paper presents a novel mathematical framework using the Lambert W function to optimize graphene nanoribbon quantum sensors for biomedical and environmental applications. By leveraging analytical solutions near branch point singularities, it achieves significant sensitivity enhancements while providing predictable design principles derived from first-principles inputs rather than purely empirical data. The work establishes a rigorous link between abstract transcendental equations and practical sensor performance across multiple modalities including magnetic field detection and gas sensing.
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Lambert W Function Framework for Graphene Nanoribbon Quantum Sensing: Theory, Verification, and Multi-Modal Applications

arXiv3 min read

arXiv:2601.10767v2 Announce Type: replace-cross 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 $f(W)=We^W$, provides exact analytical solutions to transcendental equations governing quantum confinement. Operating near the branch point singularity at $z=-1/e$ yields sensitivity enhancement factors scaling as $(z-z_c)^{-1/2}$, achieving 35-fold enhancement when the operating point lies within $\delta=0.001$ of the branch point. Comprehensive numerical verification confirms: (i) all seven bound states for strength parameter $R=10$ satisfy the constraint $u^2+v^2=R^2$ to machine precision; (ii) the theoretical band gap formula $E_g=2\pi\hbar v_F/(3W)$ is analytically equivalent to the independently determined empirical relation $E_g=1.38/W$~eV$\cdot$nm, establishing the validity of the FSW-GNR analogy; (iii) a universal sensitivity factorization $S_X = \mathcal{G}_k \cdot \eta_{\rm enh} \cdot \mathcal{P}_X$ applies 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 analytically predictable design principles for next-generation graphene quantum sensors. The framework is analytic and predictive rather than microscopic or experimental: band-structure and adsorption parameters are taken as inputs from tight-binding, first-principles, and experimental sources, and the framework returns closed-form sensitivity and design relations built upon them. Reported detection limits are labelled throughout as either literature-demonstrated device values or values predicted by the present framework.

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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 $f(W)=We^W$, provides exact analytical solutions to transcendental equations governing quantum confinement. Operating near the branch point singularity at $z=-1/e$ yields sensitivity enhancement factors scaling as $(z-z_c)^{-1/2}$, achieving 35-fold enhancement when the operating point lies within $\delta=0.001$ of the branch point. Comprehensive numerical verification confirms: (i) all seven bound states for strength parameter $R=10$ satisfy the constraint $u^2+v^2=R^2$ to machine precision; (ii) the theoretical band gap formula $E_g=2\pi\hbar v_F/(3W)$ is analytically equivalent to the independently determined empirical relation $E_g=1.38/W$~eV$\cdot$nm, establishing the validity of the FSW-GNR analogy; (iii) a universal sensitivity factorization $S_X = \mathcal{G}_k \cdot \eta_{\rm enh} \cdot \mathcal{P}_X$ applies 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 analytically predictable design principles for next-generation graphene quantum sensors. The framework is analytic and predictive rather than microscopic or experimental: band-structure and adsorption parameters are taken as inputs from tight-binding, first-principles, and experimental sources, and the framework returns closed-form sensitivity and design relations built upon them. Reported detection limits are labelled throughout as either literature-demonstrated device values or values predicted by the present framework.

Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Materials Science (cond-mat.mtrl-sci); Quantum Physics (quant-ph)

arXiv:2601.10767 [cond-mat.mes-hall]

(or arXiv:2601.10767v2 [cond-mat.mes-hall] for this version)

https://doi.org/10.48550/arXiv.2601.10767 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1016/j.rineng.2026.112238 Focus to learn more DOI(s) linking to related resources

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Full text under the publisher's open licence · 373 words

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