Towards a greener photovoltaic industry: Enhancing efficiency, environmental sustainability and manufacturing costs through solvent optimization in organic solar cells.

The energy sector is a major contributor to global greenhouse gas emissions, necessitating a transition to renewable energy sources. The photovoltaic industry plays a crucial role in this transition by harnessing solar energy, a clean and abundant resource. However, the high cost of solar panels remains a challenge. Organic solar cells (OSCs) offer a promising alternative to conventional silicon-based cells due to their low production costs and flexibility. In this study, we focus on optimizing the performance of OSCs by investigating the influence of solvent volume in the hole transport layer. By gradually increasing the volume of dimethylformamide (DMF), dimethyl sulfoxide (DMSO), and tetrahydrofuran (THF) in the PEDOT:PSS layer, we analyse the impact on power conversion efficiency (PCE) and assess the economic and environmental implications. Our results demonstrate the potential for enhancing the PCE of OSCs through solvent optimization, thereby advancing the greening of the photovoltaic industry while considering economic viability and environmental sustainability.

Reduced Graphene Oxide Inserted into PEDOT:PSS Layer to Enhance the Electrical Behaviour of Light-Emitting Diodes.

In this study, poly(9-vinylcarbazole) (PVK)-based LEDs doped with reduced graphene oxide (rGO) and cadmium sulphide (CdS) nanocrystals were fabricated by spin-coating. The hybrid LED structure was a layer sequence of glass/indium tin oxide (ITO)/PEDOT:PSS|rGO/PVK/Al. rGO was included in the poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS) layer due to its energy bands being close to PEDOT:PSS bands, and the possibility of using water for dispersing both polymer and flakes. Optical properties such as photoluminescence and UV-Vis absorption were not affected by the addition of rGO to the PEDOT:PSS solution. However, PVK-based LEDs with rGO showed increased current density compared to those without rGO in the hole transporting layer. Higher electroluminescence intensities were observed for rGO-enriched LEDs, although the shape of the spectrum was not modified. LEDs including CdS nanocrystals in the poly(9-vinylcarbazole) emissive layer did not show such dependence on the rGO presence. Though the addition of rGO to PEDOT:PSS still produces a slightly higher current density in CdS doped LEDs, this growth is no longer proportional to the rGO load.

Visual Information Fusion through Bayesian Inference for Adaptive Probability-Oriented Feature Matching.

This work presents a visual information fusion approach for robust probability-oriented feature matching. It is sustained by omnidirectional imaging, and it is tested in a visual localization framework, in mobile robotics. General visual localization methods have been extensively studied and optimized in terms of performance. However, one of the main threats that jeopardizes the final estimation is the presence of outliers. In this paper, we present several contributions to deal with that issue. First, 3D information data, associated with SURF (Speeded-Up Robust Feature) points detected on the images, is inferred under the Bayesian framework established by Gaussian processes (GPs). Such information represents a probability distribution for the feature points’ existence, which is successively fused and updated throughout the robot’s poses. Secondly, this distribution can be properly sampled and projected onto the next 2D image frame in t+1, by means of a filter-motion prediction. This strategy permits obtaining relevant areas in the image reference system, from which probable matches could be detected, in terms of the accumulated probability of feature existence. This approach entails an adaptive probability-oriented matching search, which accounts for significant areas of the image, but it also considers unseen parts of the scene, thanks to an internal modulation of the probability distribution domain, computed in terms of the current uncertainty of the system. The main outcomes confirm a robust feature matching, which permits producing consistent localization estimates, aided by the odometer’s prior to estimate the scale factor. Publicly available datasets have been used to validate the design and operation of the approach. Moreover, the proposal has been compared, firstly with a standard feature matching and secondly with a localization method, based on an inverse depth parametrization. The results confirm the validity of the approach in terms of feature matching, localization accuracy, and time consumption.

Improved Omnidirectional Odometry for a View-Based Mapping Approach.

This work presents an improved visual odometry using omnidirectional images. The main purpose is to generate a reliable prior input which enhances the SLAM (Simultaneous Localization and Mapping) estimation tasks within the framework of navigation in mobile robotics, in detriment of the internal odometry data. Generally, standard SLAM approaches extensively use data such as the main prior input to localize the robot. They also tend to consider sensory data acquired with GPSs, lasers or digital cameras, as the more commonly acknowledged to re-estimate the solution. Nonetheless, the modeling of the main prior is crucial, and sometimes especially challenging when it comes to non-systematic terms, such as those associated with the internal odometer, which ultimately turn to be considerably injurious and compromise the convergence of the system. This omnidirectional odometry relies on an adaptive feature point matching through the propagation of the current uncertainty of the system. Ultimately, it is fused as the main prior input in an EKF (Extended Kalman Filter) view-based SLAM system, together with the adaption of the epipolar constraint to the omnidirectional geometry. Several improvements have been added to the initial visual odometry proposal so as to produce better performance. We present real data experiments to test the validity of the proposal and to demonstrate its benefits, in contrast to the internal odometry. Furthermore, SLAM results are included to assess its robustness and accuracy when using the proposed prior omnidirectional odometry.