Bingxin Yang | Searcharxiv

arXiv2026

How is a gas sensor poisoned by volatile methylsiloxanes?

Volatile methyl siloxanes (VMSs), widely present in consumer and industrial products, have attracted increasing concerns due to their persistence, bioaccumulation behavior, and adverse health effects. Beyond their environmental implications, VMSs also pose operational challenges for sensing technologies because they readily decompose on sensing materials to form silicon-based compounds (e.g., silica and silane) that irreversibly impair sensing performance, a phenomenon commonly known as siloxane poisoning. Despite its prevalence, the mechanistic basis of this deactivation remains poorly understood. Herein, we present the first comprehensive theoretical study of siloxane-induced poisoning in catalytic gas sensors. Guided by our self-developed AI Agent, Digital Sensor Platform (DigSen), we first identify siloxane poisoning as a previously overlooked yet high-impact research direction. Using hexamethyldisiloxane (HMDS) as a model compound, we then conducted first-principles calculations to uncover decomposition pathways across noble metal surfaces. Strikingly, a descriptor-based microkinetic volcano model is developed to capture the trade-off between sensing activity and resistance to poisoning, enabling predictive identification of anti-poisoning candidates. These insights not only elucidate the origin of siloxane poisoning but also demonstrate how AI-driven discovery, mechanistic theory, and experiments can be integrated into a closed-loop framework for catalytic sensor design. More broadly, this AI-guided paradigm represents a generalizable strategy for materials digital discovery, offering a transferable methodology that extends well beyond siloxane systems to diverse classes of materials challenges.

physics.chem-ph↗

arXiv2025

On the Inversion Modulo a Power of an Integer

Recently, Ko\c{c} proposed a neat and efficient algorithm for computing \[ x = a^{-1} \pmod {p^k} \] for a prime $p$ based on the exact solution of linear equations using $p$-adic expansions. The algorithm requires only addition and right shift per step. In the first part of this paper, we design an algorithm that computes \[ x = a^{-1} \pmod {n^k} \] for any integers $a, n>1$ with $\gcd(a, n)=1$. The algorithm has a motivation from the schoolbook multiplication and achieves both efficiency and generality. The greater flexibility of our algorithm is explored by utilizing the built-in arithmetic of computer architecture, e.g., $n=2^{64}$, and experimental results show significant improvements. This paper also contains some results on modular inverse based on an alternative proof of correctness of Ko\c{c} algorithm. For the computation of modular inverses when the modulus is a special power of a prime $p$ (i.e., of the form $p^{2^s}$), an efficient algorithm was developed by Dumas and later improved by Hurchalla. These methods are based on Hensel lifting and perform particularly well when $p=2$ and $2^s$ matches the native bit width of a computer. In the second part of the paper, we present a generalization of these methods to moduli of the form $n^{2^s}$ for any integer $n>1$. The derivation of our algorithm follows from a simple algebraic manipulation.

cs.DS↗