Flexoelectricity Driven Fano Resonance in Slotted Carbon Nanotubes for Decoupled Multifunctional Sensing

Multifunctionality, interference-free signal readout, and quantum effect are important considerations for flexible sensors equipped within a single unit towards further miniaturization. To address these criteria, we present the slotted carbon nanotube (CNT) junction features tunable Fano resonance driven by flexoelectricity, which could serve as an ideal multimodal sensory receptor. Based on extensive ab initio calculations, we find that the effective Fano factor can be used as a temperature-insensitive extrinsic variable for sensing the bending strain, and the Seebeck coefficient can be used as a strain-insensitive intrinsic variable for detecting temperature. Thus, this dual-parameter permits simultaneous sensing of temperature and strain without signal interference. We further demonstrate the applicability of this slotted junction to ultrasensitive chemical sensing which enables precise determination of donor-type, acceptor-type, and inert molecules. This is due to the enhancement or counterbalance between flexoelectric and chemical gating. Flexoelectric gating would preserve the electron–hole symmetry of the slotted junction whereas chemical gating would break it. As a proof-of-concept demonstration, the slotted CNT junction provides an excellent quantum platform for the development of multistimuli sensation in artificial intelligence at the molecular scale.

A Computational Method Involving Surface Area to Volume Ratio to Estimate Inorganic Nanoparticle Efficacy

Inorganic nanoparticles are utilized for therapeutic, diagnostic, or in combination, theranostic purposes. The latter involves simultaneous sensing, imaging, or tracking of drug delivery. Furthermore, these nanoparticles can differ in their morphologies, which affect outcomes such as the effectiveness of hyperthermia, induction, drug loading, circulation time by escaping the body's immune system, imaging modality clarity, and biosensing. However, design of these theranostics is limited by the lack of a method to predict their therapeutic efficacy. Herein, we report a simple and novel computational approach via algebraic and geometric calculations of surface area (SA) to volume (V) ratios (SA:V) which can help predict the efficacy of the inorganic nanoparticles of the investigated morphologies. The approach comprises a coding platform for the program and uses Python 3 on a Windows 10 operating system. Analyses of 29 polyhedral morphologies that inorganic nanoparticles could assume ex silico showed that only particular concave and convex morphologies in this size regime are more productive over the standard sizes as well as a few noted in literature for baseline comparison. Our results provide a method that can aid in predicting the efficacy of inorganic nanoparticles with certain morphology giving rise to their fundamental basis and eventual implementation ex silico.

A Computational Method Involving Surface Area to Volume Ratio to Estimate Inorganic Nanoparticle Efficacy

Inorganic nanoparticles are utilized for therapeutic, diagnostic, or theranostic purposes and the latter involve simultaneous sensing, imaging, or tracking of drug delivery. Further, these nanoparticles differ in their morphologies, which affect outcomes such as the effectiveness of hyperthermia, induction, drug loading, circulation time by escaping the body's immune system, imaging modality clarity, and biosensing. However, design of these theranostics is limited by the lack of a method to predict their therapeutic efficacy. Herein, we report a computational approach involving the surface area (SA) to volume (V) ratios (SA:V), which can help predict the efficacy of the inorganic nanoparticles. The approach comprises a coding platform for the comparator pro-gram and uses a Python 3 on a Windows 10 operating system. Analyses of 22 polyhedral morphologies that inorganic nanoparticles could assume ex silico showed that only particular concave morphologies in this size regime are more productive over the standard sizes. Our results provide a method that can aid in the predicting efficacy of inorganic nanoparticles with certain morphology.

Simultaneous Sensing of Codeine and Diclofenac in Water Samples Using an Electrochemical Bi-MIP Sensor and a Voltammetric Electronic Tongue

Codeine and diclofenac overdoses have been widely reported. Here, a biomimetic sensor (bi-MIP) was devised, and an electronic tongue was used to analyze water samples simultaneously containing both these drugs. The bi-MIP sensor limits of detection for diclofenac and codeine taken individually were 0.01 µg/mL and 0.16 µg/mL, respectively. Due to a cross-reactivity effect when using the bi-MIP sensor, the electronic tongue was shown to differentiate samples containing both analytes. The results confirm the feasibility of simultaneous detection of two target analytes via a bi-MIP sensor. Additionally, they demonstrate the ability of a multi-sensor to classify different water samples.