Search

Achieving dynamic manipulation and control of single molecules at high spatio-temporal resolution is pivotal for advancing atomic-scale computing and nanorobotics. However, this endeavour is critically challenged by complex nature of atomic and molecular interactions, high-dimensional characteristics of nanoscale systems, and scarcity of experimental data. Here, we present a toy model for controlling single-molecule diffusion by harnessing electrostatic forces arising from elementary surface charges within a lattice structure, mimicking embedded charges on a surface. We investigate the interplay between quantum mechanics and electrostatic interactions in single molecule diffusion processes using a combination of state-dependent diffusion equations and Green's functions. We find that surface charge density critically influences diffusion coefficients, exhibiting linear scaling akin to Coulombic forces. We achieve accurate predictions of experimental diffusion constants and extending the observed range to values reaching up to 6000 $\mu\text{m}^2\text{ms}^{-1}$ and 80000 $\mu\text{m}^2\text{ms}^{-1}$. The molecular trajectories predicted by our model bear resemblance to planetary motion, particularly in their gravity-assisted acceleration-like behaviour. It holds transformative implications for nanorobotics, motion control at the nanoscale, and computing applications, particularly in the areas of molecular and quantum computing where the trapping of atoms and molecules is essential. Beyond the state-of-the-art optical lattice and scanning tunnelling microscopy for atomic/molecular manipulation, our findings give unambiguous advantage of precise control over single-molecule dynamics through quantum manipulation at the angstrom scale.

In several countries wearing multiple surgical masks or N95 respirators was mandatory in public during the COVID pandemic. In this study, we investigated the transportation and filtering mechanism of heterogeneous nanoparticles and viruses through surgical masks and N95 respirators. We conducted experiments in vitro using aerosol spray paints containing nanoparticles and validated the findings in vivo on a human volunteer. Scanning electron microscopy was employed to analyse the transportation and distribution of nanoparticles in different mask layers and on pristine silicon substrates placed on human skin. We provide analytical insights into the pressure distribution and fluid velocity profiles within the complex polymer network. Remarkably, our results showed that both single surgical masks and N95 respirators demonstrated similar efficiency in filtering colloidal and jet-stream nanoparticles in the air. These comprehensive findings have significant implications for policymakers in defining regulations for airborne pandemics and air pollution control.

A significantly large scope is available for the scientific and engineering developments of high-throughput ultra-high sensitive oxygen sensors. We give a perspective of oxygen sensing for two physical states of matters - solid-state nanomaterials and plasma. From single-molecule experiments to material selection, we reviewed various aspects of sensing, such as capacitance, photophysics, electron mobility, response time, and a yearly progress. Towards miniaturisation, we have highlighted the benefit of lab-on-chip-based devices and showed exemplary measurements of fast real-time oxygen sensing. From the physical-chemistry perspective, plasma holds a strong potential in the application of oxygen sensing. We investigated the current state-of-the-art of electron density, temperature, and design issues of plasma systems. We also show a numerical aspects of low-cost approach towards developing plasma-based oxygen sensor from household candle flame. In this perspective, we give an opinion about a diverse range of scientific insight together, identifies the short comings, and opens the path for new physical-chemistry device developments of oxygen sensor along with providing a guideline for innovators in oxygen sensing.

Complex pattern formation is an essential characteristic of plants and their ageing, growth, and evolution. Perception of these patterns is an intrinsic nature of plant-dependent animals for coexistence. Areca nut consisting of complex patterns is considered to be addictive for humans and has increased adverse health effects. However, no critical study is performed on the complex pattern of the areca nut. A large number of areca nuts has been studied since 2017 to develop a low-cost tool for the LMICs to categorise areca nuts. We present the first finding to identify similarities among complex networks of differently aged areca nuts by investigating the internal patterns of randomly chosen nuts from the same age group. We developed a smartphone camera-based high-resolution measurement with comprehensive biophysical mathematics and a quantum mechanical concept called density of states (DOS). We found that the DOS can provide a unique coefficient to represent age and ageing together using a single number. The average of these single numbers for less aged nuts and highly aged nuts are 4.9 and 3.8, respectively. If fruit looks aged from its external morphology as well as internal morphology, our method identifies the intrinsic similarities among the ageing networks without implementing any computationally expensive search algorithm. We show clear evidence of the diversity of ageing from relative and absolute colour vision perspectives. We have also conducted further analyses of local DOS, Fourier decomposition, correlation study, spectral decomposition, and structural similarity index.