Transport & Nanostructures Group

Our first publication of 2026 “Accelerated Discovery of Graphene Kirigami with Enhanced Elastocaloric Effect via Machine Learning” has just appeared in Nano Letters, published by the American Chemical Society.

Two-dimensional materials, particularly graphene, have captivated scientific interest due to their extraordinary properties. One promising structural paradigm is graphene kirigami (GK), where precise nanocuts are introduced into the graphene lattice to form complex architectures inspired by the Japanese art of paper cutting. Recent experimental advances have demonstrated that GK can be fabricated through top-down patterning strategies, enabling precise control over cut geometry. Specific motifs in graphene kirigami structures can display effective elastic constants spanning several orders of magnitude. In addition to altering the mechanical flexibility of graphene, kirigami patterns may also allow control of thermomechanical properties, including the elastocaloric effect, in which a material undergoes a reversible temperature change upon a change in strain due to a compressive or tensile stress, under adiabatic conditions.

Recent studies have examined the elastocaloric response of graphene kirigami (GK) and shown how it may be tailored through geometric design. This tunability makes GK a promising platform for applications in nanoscale solid-state thermal devices. In this work, we combined molecular dynamics (MD) simulations and machine learning (ML) to explore how GK geometries affect the elastocaloric coefficient (ECC), defined as the adiabatic ratio between temperature change and applied tensile stress. A data set of 16,807 GK configurations was generated through systematic cut patterns and evaluated via MD at room temperature. Using this data, both classical and deep-learning models were trained, with a convolutional neural network (CNN) achieving the best performance. Finally, a model-guided optimization identified high-ECC designs 10 times faster than random search, demonstrating the power of ML-assisted strategies for the accelerated discovery of advanced elastocaloric materials.

The results stem from the thesis work of Ph.D student Franklin Ferreira da Silva Filho.

Our latest publication of 2025 “Consensus effects of social media synthetic influence groups on scale-free networks” has just appeared in Chaos, Solitons & Fractals.

Online platforms for social interactions are an essential part of modern society. With the advance of technology and the rise of AI algorithms, content is now filtered systematically, facilitating the formation of filter bubbles. This work investigates the social consensus under limited visibility in a two-state majority-vote model on Barabási–Albert scale-free networks. In the consensus evolution, each individual assimilates the opinion of the majority of their neighbors with probability (1-q) and disagrees with chance q, known as the noise parameter. We define a visibility parameter as the probability of an individual considering the opinion of a neighbor at a given interaction. This parameter enables us to model the limited visibility phenomenon that produces synthetic neighborhoods in online interactions. We employ Monte Carlo simulations and finite-size scaling analysis to obtain the critical noise parameter as a function of the visibility and the network growth parameter. We also find the critical exponents of the system and validate the unitary relation for complex networks. Our analysis shows that installing and manipulating synthetic influence groups critically undermines consensus robustness, leading to serious consequences in the modern society.

Although this is not our main line of work, it is a type of problem I also enjoy working on. The results stem from the work of graduate student Giuliano Porciúncula, who is supervised by my colleague André L. M. Vilela at UPE.

Our latest paper of 2024 “Lévy flight for electrons in graphene in the presence of regions with enhanced spin-orbit coupling”  has just appeared in Physical Review B.

In this work, we propose an electronic Lévy glass built from graphene nanoribbons in the presence of regions with enhanced spin-orbit coupling. Although electrons in graphene nanoribbons present a low spin-orbit coupling strength, it can be increased by a proximity effect with an appropriate substrate. We consider graphene nanoribbons with different edge types, which contain circular regions with a tunable Rashba spin-orbit coupling, whose diameter follows a power-law distribution. We find that spin-orbital clusters induce a transition from superdiffusive to diffusive charge transport, similar to what we recently reported for nanoribbons with electrostatic clusters [Phys. Rev. B 107, 155432 (2023)]. We also investigate spin polarization in the spin-orbital Lévy glasses, and show that a finite spin polarization can be found only in the superdiffusive regime. In contrast, the spin polarization vanishes in the diffusive regime, making the electronic Lévy glass a useful device whose electronic transmission and spin polarization can be controlled by its Fermi energy. Finally, we apply a multifractal analysis to charge transmission and spin polarization, and find that the transmission time series in the superdiffusive regime are multifractal, while they tend to be monofractal in the diffusive regime. In contrast, spin polarization time series are multifractal in both regimes, characterizing a marked difference between mesoscopic fluctuations of charge transport and spin polarization in the proposed electronic Lévy glass.

The results stem from the work of new Ph.D student Diego B. Fonseca, which is co-supervised by Anderson L. R. Barbosa at UFRPE.

Our latest publication “Length and torsion dependence of thermal conductivity in twisted graphene nanoribbons” has just been accepted for publication in Physical Review Materials.

In this work we investigate the dependence of the thermal conductivity (TC) of twisted graphene nanoribbons (TGNRs) on the number of applied turns to the GNR by calculating more precise and mathematically well defined geometric parameters related to the TGNR shape, namely, its twist and writhe. We show that the dependence of the TC on twist is not a simple function of the number of turns initially applied to a straight GNR. In fact, we show that the TC of TGNRs requires at least two parameters to be properly described. Our conclusions are supported by atomistic molecular dynamics simulations to obtain the TC of suspended TGNRs prepared under different values of initially applied turns and different sizes of their suspended part. Among possible choices of parameter pairs, we show that TC can be appropriately described by the initial number of turns and the initial twist density of the TGNRs.

The work is a collaboration with Alexandre Fonseca at UNICAMP.

Our first paper of 2024 “Tuning the thermal conductivity of silicon phononic crystals via different defect motifs: Implications for thermoelectric devices and photovoltaics” has just been accepted for publication in ACS Applied Nano Materials, where it will be part of a Forum Focused on South American Authors jointly published with ACS Applied Materials & Interfaces. This joint special issue will be a collection of articles published in a single issue of the journal authored by leaders of applied materials in South America. Apparently, for editorial reasons this volume of the journal was published with 2025 as year of publication 🤷‍♂️.

In this publication we investigate the thermal conductivity of silicon phononic crystals, materials with a periodic arrangement of modifications which can be tailored to control their thermal conductivity. The introduction of intermediate-sized pores in silicon membranes can reduce their lattice thermal conductivity and increase their thermoelectric efficiency, as long as the pores are not too small to interfere with electron transport, nor too large to cause structural instabilities in the material. We consider thin silicon membranes and structures with holes of different sizes and shapes, forming phononic crystals with different defect motifs, and calculate their thermal conductivity along the [110] and [-110] crystalline directions, with the homogeneous non-equilibrium molecular dynamics method. We find that for the hole sizes considered the thermal conductivity is a logarithmically decreasing function of the defect area, independent of defect shape and transport direction, and verify that the thermal conductivity of silicon phononic crystals is ultimately limited by the neck size, which is the smallest distance between two adjacent defects, in agreement with the literature. However, our results also show that the dependence of the conductivity with neck size follows a power law along the [110] direction, but shows an exponential dependence along the [-110] direction. We attribute this difference to the scattering of phonons by surface dimers which originate in the 2×1 reconstruction of the silicon [001] surface, and are oriented along the [-110], acting as resonators which can scatter phonon more efficiently along that direction. Those findings are relevant for the design of thermoelectric devices and thermal barriers in photovoltaic cells.

The results stem from Higo de Araujo Oliveira mater’s thesis, and it is the first graduate thesis from our group since we moved to UFPE in 2020. It is also another great result from our ongoing collaboration with Ari Harju and Zheyong Fan, both currently at Varian Medical Systems in Finland.