Julian Sutaria | Searcharxiv

arXiv2025

Quantum Transport and Molecular Sensing in Reduced Graphene Oxide Measured with Scanning Probe Microscopy

We report combined scanning probe microscopy and electrical measurements to investigate local electronic transport in reduced graphene oxide (rGO) devices. We demonstrate that quantum transport in these materials can be significantly tuned by the electrostatic potential applied with a conducting atomic force microscope (AFM) tip. Scanning gate microscopy (SGM) reveals a clear p-type response in which local gating modulates the source-drain current, while scanning impedance microscopy (SIM) indicates corresponding shifts of the Fermi level under different gating conditions. The observed transport behavior arises from the combined effects of the AFM tip induced Fermi level shifts and defect mediated scattering. These results show that resonant scattering associated with impurities or structural defects plays a central role and highlight the strong influence of local electrostatic potentials on rGO conduction. Consistent with this electrostatic control, the device also exhibits chemical gating and sensing: during exposure to electron-withdrawing molecules (acetone), the source-drain current increases reversibly and returns to baseline upon purging with air. Repeated cycles over 15 minutes show reproducible amplitudes and recovery. Using a simple transport model we estimate an increase of about 40 percent in carrier density during exposure, consistent with p-type doping by electron-accepting analytes. These findings link nanoscale electrostatic control to macroscopic sensing performance, advancing the understanding of charge transport in rGO and underscoring its promise for nanoscale electronics, flexible chemical sensors, and tunable optoelectronic devices.

cond-mat.mes-hall↗

arXiv2025

Maximum-Entropy Model of Colored Noise in Superdiffusive Axonal Growth

We develop a coarse-grained stochastic theory for axonal growth on micropatterned substrates using the Shannon--Jaynes maximum entropy principle. Starting from a Langevin description of growth cone motion, we infer the effective distribution of traction force relaxation rates from experimentally motivated constraints rather than postulating the colored noise directly. The resulting relaxation rate distribution generates a stationary colored acceleration process with power-law temporal correlations and yields analytical predictions for the axonal mean squared displacement and velocity autocorrelation. The long-time behavior is controlled by the slow-relaxation part of the inferred distribution, corresponding physically to broadly distributed clutch or adhesion engagement times. For biologically relevant parameters, the model predicts a negative correlation exponent $\alpha=-1/2$. This prediction is in close quantitative agreement with measurements on cortical neurons cultured on micropatterned poly-D-lysine-coated PDMS substrates, which are well described by $\alpha\simeq -0.6$ and exhibit superdiffusive mean squared displacement scaling with exponent $1.4$. The same framework accounts for the crossover from early diffusive behavior to long-time anomalous growth and for the corresponding power law decay of the velocity autocorrelation. These results show how entropy-constrained active fluctuations can connect microscopic force generation processes to emergent growth laws in neuronal systems and, more broadly, in active matter.

physics.bio-ph↗