arXiv subjects

Roderick C. I. Mackenzie

Publications and source records attributed to Roderick C. I. Mackenzie.

3 recordsLinked to original sources

arXiv2026

Rethinking Charge Transport and Recombination in Donor-diluted Organic Solar Cells

We systematically investigate PM6:Y12 bulk-heterojunction solar cells with donor fractions ranging from 1% to 45%, linking morphology, charge transport, and recombination to device performance. Complementary structural and spectroscopic methods reveal that a percolating PM6 network forms even at below 5% donor content, with lamellar stacking and vertical composition gradients that do not hinder the charge extraction. The reduction of the effective active layer conductivity towards low donor fractions obeys a three-dimensional percolation model, indicating that charge transport is governed by network topology rather without a pronounced percolation threshold. A transition from nongeminate Langevin recombination to a dispersive Smoluchowski-type loss occurs below 5% donor fraction. The latter regime is also nongeminate, i.e., pertains to recombination of the total charge carrier density. Correspondingly, we observe that the Langevin reduction in the higher donor fractions - mostly dominated by redissociation of electron-hole pairs after encounter - changes towards low donor fractions: in these cases, the nongeminate loss rate exceeds the prediction of the Langevin model. This regime coincides with increasing transport resistance due to topology-limited hole conduction, leading to reduced fill factors despite a high retained charge-generation efficiency. Our results demonstrate that strong donor dilution preserves photogeneration if a continuous donor network is maintained, and unveil how topology-controlled transport and non-Langevin recombination jointly define the performance limits of donor-diluted organic solar blends.

cond-mat.mtrl-sci↗

arXiv2026

Bridging the lab-to-fab gap in non-fullerene organic solar cells via gravure printing

Organic solar cells have reached record efficiencies with non-fullerene acceptors, yet their translation to industrial printing remains a critical bottleneck. Here we report the highest efficiency achieved for a fully roll-to-roll-compatible gravure-printed non-fullerene organic solar cell. High-performance blends are typically optimised under laboratory coating conditions, while roll-to-roll manufacturing imposes fundamentally different constraints on ink stability, drying dynamics, and multilayer integration. Whether these constraints intrinsically limit device physics has remained unresolved. Here, we demonstrate a gravure-printed PM6:Y12 solar cell architecture using commercially available materials and establish a quantitative framework that disentangles optical, recombination, and transport losses in printed devices. We find that favourable bulk morphology and exciton harvesting can be preserved under gravure printing and non-halogenated solvents. The dominant efficiency penalties arise instead from optical interference within the printed layer stack and slow charge transport. Our results demonstrate that the performance gap between laboratory and printed solar cells is originating from device architecture rather than the intrinsic physics of modern non-fullerene systems, providing a mechanistic roadmap for roll-to-roll manufacturing of non-fullerene solar cells.

cond-mat.mtrl-sci↗

arXiv2025

A Framework to Pinpoint Bottlenecks in Emerging Solar Cells and Disordered Devices via Differential Machine Learning

A key challenge in the development of materials for the next generation of solar cells, sensors and transistors is linking macroscopic device performance to underlying microscopic properties. For years, fabrication of devices has been faster than our ability to characterize them. This has led to a random walk of material development, with new materials being proposed faster than our understanding. We present two neural network-based methods for extracting key material parameters, including charge carrier mobility and trap state density, in optoelectronic devices such as solar cells. Our methods require solely measured light current--voltage curve and modest computational resources, making our approach applicable in even minimally equipped laboratories. Unlike traditional machine learning models, our methods place the final material values in a non-Gaussian likelihood distribution, allowing confidence assessment of each predicted parameter. We demonstrate these techniques using fresh PM6:Y12 and degraded PM6:BTP-eC9 organic solar cells.

physics.app-ph↗