arXiv subjects

Cristian Staii

Publications and source records attributed to Cristian Staii.

16 recordsLinked to original sources

arXiv2026

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 $α=-1/2$. 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 $α\simeq -0.6$ 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↗

arXiv2026

Stochastic Models of Neuronal Growth

Neuronal circuits arise as axons and dendrites extend, navigate, and connect to target cells. Axonal growth, in particular, integrates deterministic guidance from substrate mechanics and geometry with stochastic fluctuations generated by signaling, molecular detection, cytoskeletal assembly, and growth cone dynamics. A comprehensive quantitative description of this process remains incomplete. We review stochastic models in which Langevin dynamics and the associates Fokker-Planck equation capture axonal motion and turning under combined biases and noise. Paired with experiments, these models yield key parameters, including effective diffusion (motility) coefficients, speed and angle distributions, mean-square displacement, and mechanical measures of cell-substrate coupling, thereby linking single-cell biophysics and intercellular interactions to collective growth statistics and network formation. We further couple the Fokker-Planck description to a mechanochemical actin-myosin-clutch model and perform a linear stability analysis of the resulting dynamics. Routh--Hurwitz criteria identify regimes of steady extension, damped oscillations, and Hopf bifurcations that generate sustained limit cycles. Together, these results clarify the mechanisms that govern axonal guidance and connectivity and inform the design of engineered substrates and neuroprosthetic scaffolds aimed at enhancing nerve repair and regeneration.

physics.bio-ph↗

arXiv2026

Entropy Production from Spin--Vibrational Coupling in Endohedral-Fullerene Qubits Encapsulated in Suspended Carbon Nanotubes

Hybrid carbon nanotube-fullerene architectures provide a controllable platform for studying irreversibility and information flow in structured quantum environments. We analyze entropy generation in a system where paramagnetic endohedral fullerenes, such as N@C$_{60}$ and P@C$_{60}$, are encapsulated inside a suspended carbon nanotube (CNT) resonator, with selected multi-level fullerene spin states forming an effective qubit coupled to quantized CNT flexural modes. Building on prior work on fullerene-filled CNTs, spin-phonon control in suspended nanotubes, and phase-space propagators for damped driven oscillators, we develop a hybrid open-system model combining driven quantum Brownian motion of the CNT with an effective Jaynes-Cummings spin-vibrational interaction. The resonator dynamics are represented by a Wigner function whose evolution is written analytically in terms of the initial Wigner distribution and a Gaussian propagator. This phase-space description separates drive-induced displacement, diffusion, and damping, and connects these processes directly to entropy flow. The coupled spin-mechanical dynamics are embedded in a Lindblad master equation including mechanical damping, spin relaxation, pure dephasing, and thermally activated excitation. Within this framework we derive the entropy balance, identify entropy flux and non-negative entropy production, and examine how spin-vibrational hybridization redistributes irreversibility between coherent exchange and dissipative channels. We show that magnetic-gradient-enhanced spin-phonon coupling, resonant driving, and moderate thermal occupation produce crossovers between oscillator-dominated and spin-dominated entropy-production regimes. The framework provides a basis for using CNT-PEF hybrids as nanoscale platforms to study nonequilibrium quantum thermodynamics, decoherence, and information loss in vibrational environments.

cond-mat.mes-hall↗

arXiv2026

Quantum Tomography of Suspended Carbon Nanotubes

We propose and analyze an all-mechanical route to coherent control and quantum-state reconstruction of the fundamental flexural mode of a suspended carbon nanotube (CNT) operated in the anharmonic (Duffing/Kerr). A nearby atomic force microscope (AFM) provides a single, localized actuator that applies calibrated, time-dependent forces to the CNT. In the presence of mechanical anharmonicity this enables spectrally selective control of the lowest vibrational transition and thus supports effective two-level protocols such as Rabi oscillations and Ramsey interferometry. The same actuator also implements phase-space displacements required for Wigner function tomography via displaced-parity sampling, thereby unifying control and tomography without optical heating and without dedicated on-chip microwave drive lines at the CNT resonator. We develop explicit pulse sequences and a master equation framework that connect experimentally accessible signals to energy relaxation and phase coherence times and to parity-based quantum signatures, including negative regions of the Wigner function. The approach is compatible with multiple readout modalities, including direct AFM-based detection and dispersive coupling to superconducting circuitry such as Cooper-pair box, and/or a microwave cavity. Together, these techniques provide complete access to populations, coherence, and parity within a single device architecture. This minimal scheme provides a practical route to all-mechanical quantum control and state-resolved characterization of decoherence in mesoscopic mechanical systems.

quant-ph↗

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

Feedback-Driven Dynamical Model for Axonal Extension on Parallel Micropatterns

Despite significant advances in understanding neuronal development, a fully quantitative framework that integrates intracellular mechanisms with environmental cues during axonal growth remains incomplete. Here, we present a unified biophysical model that captures key mechanochemical processes governing axonal extension on micropatterned substrates. In these environments, axons preferentially align with the pattern direction, form bundles, and advance at constant speed. The model integrates four core components: (i) actin-adhesion traction coupling, (ii) lateral inhibition between neighboring axons, (iii) tubulin transport from soma to the growth cone, and (4) orientation dynamics guided by the substrate anisotropy. Dynamical systems analysis reveals that the saddle-node bifurcation in the actin adhesion subsystem drives a transition to a high-traction motile state, while traction feedback shifts a pitchfork bifurcation in the signaling loop, promoting symmetry breaking and robust alignment. An exact linear solution in the tubulin transport subsystem functions as a built-in speed regulator, ensuring stable elongation rates. Simulations using experimentally inferred parameters accurately reproduce elongation speed, alignment variance, and bundle spacing. The model provides explicit design rules for enhancing axonal alignment through modulation of substrate stiffness and adhesion dynamics. By identifying key control parameters, this work enables rational design of biomaterials for neural repair and engineered tissue systems.

q-bio.NC↗

arXiv2022

Neuronal Growth and Formation of Neuron Networks on Directional Surfaces

The formation of neuron networks is a process of fundamental importance for understanding the development of the nervous system and for creating biomimetic devices for tissue engineering and neural repair. The basic process that controls the network formation is the growth of an axon from the cell body and its extension towards target neurons. Axonal growth is directed by environmental stimuli that include intercellular interactions, biochemical cues, and the mechanical and geometrical properties of the growth substrate. Despite significant recent progress, the steering of the growing axon remains poorly understood. In this paper, we develop a model of axonal motility, which incorporates substrate-geometry sensing. We combine experimental data with theoretical analysis to measure the parameters that describe axonal growth on micropatterned surfaces: diffusion (cell motility) coefficients, speed and angular distributions, and cell-substrate interactions. Experiments performed on neurons treated with inhibitors for microtubules (Taxol) and actin filaments (Y-27632) indicate that cytoskeletal dynamics play a critical role in the steering mechanism. Our results demonstrate that axons follow geometrical patterns through a contact-guidance mechanism, in which geometrical patterns impart high traction forces to the growth cone. These results have important implications for bioengineering novel substrates to guide neuronal growth and promote nerve repair.

q-bio.CB↗

arXiv2019

Role of geometrical cues in neuronal growth

Geometrical cues play an essential role in neuronal growth. Here, we quantify axonal growth on surfaces with controlled geometries and report a general stochastic approach that quantitatively describes the motion of growth cones. We show that axons display a strong directional alignment on micro-patterned surfaces when the periodicity of the patterns matches the dimension of the growth cone. The growth cone dynamics on surfaces with uniform geometry is described by a linear Langevin equation with both deterministic and stochastic contributions. In contrast, axonal growth on surfaces with periodic patterns is characterized by a system of two generalized Langevin equations with both linear and quadratic velocity dependence and stochastic noise. We combine experimental data with theoretical analysis to measure the key parameters of the growth cone motion: angular distributions, correlation functions, diffusion coefficients, characteristics speeds and damping coefficients. We demonstrate that axonal dynamics displays a cross-over from an Ornstein-Uhlenbeck process to a non-linear stochastic regime when the geometrical periodicity of the pattern approaches the linear dimension of the growth cone. Growth alignment is determined by surface geometry, which is fully quantified by the deterministic part of the Langevin equation. These results provide new insight into the role of curvature sensing proteins and their interactions with geometrical cues.

physics.bio-ph↗

arXiv2019

Anomalous diffusion for neuronal growth on surfaces with controlled geometries

Geometrical cues are known to play a very important role in neuronal growth and the formation of neuronal networks. Here, we present a detailed analysis of axonal growth and dynamics for neuronal cells cultured on patterned polydimethylsiloxane surfaces. We use fluorescence microscopy to image neurons, quantify their dynamics, and demonstrate that the substrate geometrical patterns cause strong directional alignment of axons. We quantify axonal growth and report a general stochastic approach that quantitatively describes the motion of growth cones. The growth cone dynamics is described by Langevin and Fokker-Planck equations with both deterministic and stochastic contributions. We show that the deterministic terms contain both the angular and speed dependence of axonal growth, and that these two contributions can be separated. Growth alignment is determined by surface geometry, and it is quantified by the deterministic part of the Langevin equation. We combine experimental data with theoretical analysis to measure the key parameters of the growth cone motion: speed and angular distributions, correlation functions, diffusion coefficients, characteristics speeds and damping coefficients. We demonstrate that axonal dynamics displays a cross-over from Brownian motion (Ornstein-Uhlenbeck process) at earlier times to anomalous dynamics (superdiffusion) at later times. The superdiffusive regime is characterized by non-Gaussian speed distributions and power law dependence of the axonal mean square length and the velocity correlation functions. These results demonstrate the importance of geometrical cues in guiding axonal growth, and could lead to new methods for bioengineering novel substrates for controlling neuronal growth and regeneration.

q-bio.CB↗

arXiv2013

Neuronal Growth as Diffusion in an Effective Potential

Current understanding of neuronal growth is mostly qualitative, as the staggering number of physical and chemical guidance cues involved prohibit a fully quantitative description of axonal dynamics. We report on a general approach that describes axonal growth in vitro, on poly-D-lysine coated glass substrates, as diffusion in an effective external potential, representing the collective contribution of all causal influences on the growth cone. We use this approach to obtain effective growth rules that reveal an emergent regulatory mechanism for axonal pathfinding on these substrates.

physics.bio-ph↗

arXiv2013

Temperature response of the neuronal cytoskeleton mapped via atomic force and fluorescence microscopy

Neuronal cells change their growth properties in response to external physical stimuli such as variations in external temperature, stiffness of the growth substrate, or topographical guidance cues. Detailed knowledge of the mechanisms that control these biomechanical responses is necessary for understanding the basic principles that underlie neuronal growth and regeneration. Here, we present elasticity maps of living cortical neurons (embryonic rat) as a function of temperature, and correlate these maps to the locations of internal structural components of the cytoskeleton. Neurons display a significant increase in the average elastic modulus upon a decrease in ambient temperature from 37°C to 25°C. We demonstrate that the dominant mechanism by which the elasticity of the neurons changes in response to temperature is the stiffening of the actin components of the cytoskeleton induced by myosin II. We also report a reversible shift in the location and composition of the high-stiffness areas of the neuron cytoskeleton with temperature. At 37°C the areas of the cell displaying high elastic modulus overlap with the tubulin-dense regions, while at 25°C these high-stiffness areas correspond to the actin-dense regions of the cytoskeleton. These results demonstrate the importance of considering temperature effects when investigating cytoskeletal dynamics in cells.

physics.bio-ph↗

arXiv2013

Neuronal Alignment On Asymmetric Textured Surfaces

Axonal growth and the formation of synaptic connections are key steps in the development of the nervous system. Here we present experimental and theoretical results on axonal growth and interconnectivity in order to elucidate some of the basic rules that neuronal cells use for functional connections with one another. We demonstrate that a unidirectional nanotextured surface can bias axonal growth. We perform a systematic investigation of neuronal processes on asymmetric surfaces and quantify the role that biomechanical surface cues play in neuronal growth. These results represent an important step towards engineering directed axonal growth for neuro-regeneration studies.

physics.bio-ph↗

arXiv2013

Kelvin Probe Microscopy and Electronic Transport Measurements in Reduced Graphene Oxide Chemical Sensors

Reduced Graphene Oxide (RGO) is an electronically hybrid material that displays remarkable chemical sensing properties. Here, we present a quantitative analysis of the chemical gating effects in RGO-based chemical sensors. The gas sensing devices are patterned in a field-effect transistor geometry, by dielectrophoretic assembly of RGO platelets between gold electrodes deposited on SiO2/Si substrates. We show that these sensors display highly selective and reversible responses to the measured analytes, as well as fast response and recovery times (tens of seconds). We use combined electronic transport/Kelvin Probe Microscopy measurements to quantify the amount of charge transferred to RGO due to chemical doping when the device is exposed to electron-acceptor (acetone) and electron-donor (ammonia) analytes. We demonstrate that this method allows us to obtain high-resolution maps of the surface potential and local charge distribution both before and after chemical doping, to identify local gate-susceptible areas on the RGO surface, and to directly extract the contact resistance between the RGO and the metallic electrodes. The method presented is general, suggesting that these results have important implications for building graphene and other nanomaterial-based chemical sensors.

cond-mat.mtrl-sci↗

arXiv2013

Elasticity Maps of Living Neurons Measured by Combined Fluorescence and Atomic Force Microscopy

Detailed knowledge of mechanical parameters such as cell elasticity, stiffness of the growth substrate, or traction stresses generated during axonal extensions is essential for understanding the mechanisms that control neuronal growth. Here we combine Atomic Force Microscopy based force spectroscopy with Fluorescence Microscopy to produce systematic, high-resolution elasticity maps for three different types of live neuronal cells: cortical (embryonic rat), embryonic chick dorsal root ganglion, and P-19 (mouse embryonic carcinoma stem cells) neurons. We measure how the stiffness of neurons changes both during neurite outgrowth and upon disruption of microtubules of the cell. We find reversible local stiffening of the cell during growth, and show that the increase in local elastic modulus is primarily due to the formation of microtubules. We also report that cortical and P-19 neurons have similar elasticity maps, with elastic moduli in the range 0.1-2 kPa, with typical average values of 0.4 kPa (P-19) and 0.2 kPa (cortical). In contrast, DRG neurons are stiffer than P-19 and cortical cells, yielding elastic moduli in the range 0.1-8 kPa, with typical average values of 0.9 kPa. Finally, we report no measurable influence of substrate protein coating on cell body elasticity for the three types of neurons.

physics.bio-ph↗

arXiv2005

DNA-decorated carbon nanotubes for chemical sensing

We demonstrate a new, versatile class of nanoscale chemical sensors based on single-stranded DNA (ss-DNA) as the chemical sensors recognition site and single-walled carbon nanotube field effect transistors (swCN-FET) as the electronic read-out component. swCN-FETs with a nanoscale coating of ss-DNA respond to gas odors that do not cause a detectable conductivity change in bare devices. Responses of ss-DNA/swCN-FETs differ in sign and magnitude for different gases, and can be tuned by choosing the base sequence of the ss-DNA. ss-DNA/swCN-FET sensors detect a variety of odors, with rapid response and recovery times on the scale of seconds. The sensor surface is self-regenerating: samples maintain a constant response with no need for sensor refreshing through at least 50 gas exposure cycles. This very remarkable set of attributes makes sensors based on ss-DNA decorated nanotubes very promising for "electronic nose" and "electronic tongue" applications ranging from homeland security to disease diagnosis.

cond-mat.mtrl-sci↗

arXiv2005

High frequency Scanning Gate Microscopy and local memory effect of carbon nanotube transistors

We use impedance spectroscopy to measure the high frequency properties of single-walled carbon nanotube field effect transistors (swCN-FETs). Furthermore, we extend Scanning Gate Microscopy (SGM) to frequencies up to 15MHz, and use it to image changes in the impedance of swCN-FET circuits induced by the SGM-tip gate. In contrast to earlier reports, the results of both experiments are consistent with a simple RC parallel circuit model of the swCN-FET, with a time constant of 0.3 ms. We also use the SGM tip to show the local nature of the memory effect normally observed in swCN-FETs, implying that nanotube-based memory cells can be miniaturized to dimensions of the order of tens of nm.

cond-mat.mtrl-sci↗