arXiv subjects

Pelin Çiloğlu

Publications and source records attributed to Pelin Çiloğlu.

8 recordsLinked to original sources

arXiv2026

Mobility-lifetime relation links photodegradation in spin-coated and gravure-printed organic solar cells

The degradation mechanisms of organic solar cells (OSCs) have been studied primarily in spin-coated, laboratory-scale devices, whereas scalable processing modifies the device architecture, active-layer morphology, and underlying charge-transport and recombination properties. Whether these changes also alter how solar cells degrade remains unclear. Here, we compare spin-coated and fully roll-to-roll-compatible gravure-printed PM6:Y12 solar cells during $\sim$1000 h of continuous illumination. Despite distinct initial properties and degradation signatures, the loss of power-conversion efficiency systematically follows the mobility-lifetime product $μτ$. Remarkably, ageing of the printed devices increases the recombination lifetime while strongly reducing charge-carrier mobility, showing that a longer lifetime alone does not imply improved device performance. The mobility reduction is accompanied by decreased PM6 lamellar order, whereas the additional open-circuit voltage loss originates predominantly from increased non-radiative recombination. Dark recovery further reveals a metastable contribution specific to the printed architecture. These results identify the mobility-lifetime product as a unifying physical descriptor for photodegradation, linking ageing-induced microscopic changes to macroscopic performance loss across spin-coated and scalable printed OSCs.

cond-mat.mtrl-sci↗

arXiv2026

Preconditioning for Diffuse Interface Tumor Growth Models

Computational models of tumor growth form a contemporary area of study. Diffuse interface models have been proposed as an important tool for cancer modeling. In this work, we consider preconditioners for numerical simulations of two tumor growth models based on diffuse interface models. For numerical efficiency, we propose block preconditioners that rely on using effective Schur-complement approximations. We show that these methods combined with state-of-the-art adaptive finite element discretizations lead to robust simulations in two and three dimensions. In extensive numerical experiments, we show that the proposed methods show robust convergence behavior.

math.NA↗

arXiv2026

Modeling and Simulation of Device Performance in Organic Photovoltaics

We present a pipeline to study the device performance of organic solar cells in silico. We introduce a mathematical model that includes the dynamics of excitons as well as their dissociation at bulk heterojunctions within the nanomorphology of the active layer. This is combined with realistic morphologies that we obtain from a detailed phase field model. To solve the coupled nonlinear system, we use a finite element discretization, robust linear solvers, and three numerical schemes, Newton, Gummel, and Semi--Newton--Gummel. This allows for an efficient simulation of the complete OPV device and results in current-voltage curves that can readily be compared to measured data.

math.NA↗

arXiv2025

Visualizing the Link Between Nanomorphology and Energetic Disorder in 3D Organic Solar Cells

The performance of organic bulk heterojunction (BHJ) solar cells is highly sensitive to both nanomorphology and energetic disorder arising from microscopic molecular packing and structural defects. However, most models used to understand these devices are either one-dimensional effective medium approximations that neglect spatial and energetic disorder or three-dimensional Monte Carlo simulations that are computationally intensive. In this work, we present the results from a three-dimensional hybrid model capable of operating at both high carrier densities and incorporating the effects of energetic disorder. We first generate realistic morphologies using a phase-field approach that accounts for solvent evaporation during film formation. Using these example morphologies, we systematically study the interplay between energetic disorder and configurational disorder at carrier densities representative of real device operation. This enables us to separate and visualize the impact of the nanomorphology and energetic disorder on device performance. Our results reveal that, even when macroscopic percolation pathways remain intact, energetic disorder limits performance primarily through suppressed charge extraction in interconnected domains. This suggest that optimizing molecular packing at the nanoscale is as critical as controlling phase separation at the mesoscale, highlighting the need for multiscale design strategies in next-generation BHJ devices.

physics.app-ph↗

arXiv2025

An Adaptive Algorithm Based on Stochastic Discontinuous Galerkin for Convection Dominated Equations with Random Data

In this paper, we propose an adaptive approach, based on mesh refinement or parametric enrichment with polynomial degree adaption, for numerical solution of convection dominated equations with random input data. A parametric system emerged from an application of stochastic Galerkin approach is discretized by using symmetric interior penalty Galerkin (SIPG) method with upwinding for the convection term in the spatial domain. We derive a residual-based error estimator contributed by the error due to the SIPG discretization, the (generalized) polynomial chaos discretization in the stochastic space, and data oscillations. Then, the reliability of the proposed error estimator, an upper bound for the energy error up to a multiplicative constant, is shown. Moreover, to balance the errors stemmed from spatial and stochastic spaces, the truncation error emerged from Karhunen--Loève expansion are considered in the numerical simulations. Last, several benchmark examples including a random diffusivity parameter, a random convectivity parameter, random diffusivity/convectivity parameters, and a random (jump) discontinuous diffusivity parameter, are tested to illustrate the performance of the proposed estimator.

math.NA↗

arXiv2025

Preconditioning for a Cahn-Hilliard-Navier-Stokes model for morphology formation in organic solar cells

We present a model for the morphology evolution of printed organic solar cells which occurs during the drying of a mixture of polymer, the non-fullerene acceptor and the solvent. Our model uses a phase field approach coupled to a Navier-Stokes equation describing the macroscopic movement of the fluid. Additionally, we incorporate the evaporation process of the solvent using an Allen-Cahn equation. The model is discretized using a finite-element approach with a semi-implicit discretization in time. The resulting (non)linear systems are coupled and of large dimensionality. We present a preconditioned iterative scheme to solve them robustly with respect to changes in the discretization parameters. We illustrate that the preconditioned solver shows parameter-robust iteration numbers and that the model qualitatively captures the behavior of the film morphology during drying.

math.NA↗

arXiv2022

Stochastic Discontinuous Galerkin Methods for Robust Deterministic Control of Convection Diffusion Equations with Uncertain Coefficients

We investigate a numerical behaviour of robust deterministic optimal control problem subject to a convection diffusion equation containing uncertain inputs. Stochastic Galerkin approach, turning the original optimization problem containing uncertainties into a large system of deterministic problems, is applied to discretize the stochastic domain, while a discontinuous Galerkin method is preferred for the spatial discretization due to its better convergence behaviour for optimization problems governed by convection dominated PDEs. Error analysis is done for the state and adjoint variables in the energy norm, while the estimates of deterministic control is obtained in the $L^2$--norm. Large matrix system emerging from the stochastic Galerkin method is addressed by the low--rank version of GMRES method, which reduces both the computational complexity and the memory requirements by employing Kronecker--product structure of the obtained linear system. Benchmark examples with and without control constraints are presented to illustrate the efficiency of the proposed methodology.

math.NA↗

arXiv2020

Stochastic Discontinuous Galerkin Methods with Low--Rank Solvers for Convection Diffusion Equations

We investigate numerical behaviour of a convection diffusion equation with random coefficients by approximating statistical moments of the solution. Stochastic Galerkin approach, turning the original stochastic problem to a system of deterministic convection diffusion equations, is used to handle the stochastic domain in this study, whereas discontinuous Galerkin method is used to discretize spatial domain due to its local mass conservativity. A priori error estimates of the stationary problem and stability estimate of the unsteady model problem are derived in the energy norm. To address the curse of dimensionality of Stochastic Galerkin method, we take advantage of the low--rank Krylov subspace methods, which reduce both the storage requirements and the computational complexity by exploiting a Kronecker--product structure of system matrices. The efficiency of the proposed methodology is illustrated by numerical experiments on the benchmark problems.

math.NA↗