Impact of Surface Composition Changes on the CO2-Reduction Performance of Au-Cu Aerogels.

Over the past decades, the electrochemical CO2-reduction reaction (CO2RR) has emerged as a promising option for facilitating intermittent energy storage while generating industrial raw materials of economic relevance such as CO. Recent studies have reported that Au-Cu bimetallic nanocatalysts feature a superior CO2-to-CO conversion as compared with the monometallic components, thus improving the noble metal utilization. Under this premise and with the added advantage of a suppressed H2-evolution reaction due to absence of a carbon support, herein, we employ bimetallic Au3Cu and AuCu aerogels (with a web thickness ≈7 nm) as CO2-reduction electrocatalysts in 0.5 M KHCO3 and compare their performance with that of a monometallic Au aerogel. We supplement this by investigating how the CO2RR-performance of these materials is affected by their surface composition, which we modified by systematically dissolving a part of their Cu-content using cyclic voltammetry (CV). To this end, the effect of this CV-driven composition change on the electrochemical surface area is quantified via Pb underpotential deposition, and the local structural and compositional changes are visually assessed by employing identical-location transmission electron microscopy and energy-dispersive X-ray analyses. When compared to the pristine aerogels, the CV-treated samples displayed superior CO Faradaic efficiencies (≈68 vs ≈92% for Au3Cu and ≈34 vs ≈87% for AuCu) and CO partial currents, with the AuCu aerogel outperforming the Au3Cu and Au counterparts in terms of Au-mass normalized CO currents among the CV-treated samples.

Spectroscopy vs. Electrochemistry: Catalyst Layer Thickness Effects on Operando/In Situ Measurements.

In recent years, operando/in situ X-ray absorption spectroscopy (XAS) has become an important tool in the electrocatalysis community. However, the high catalyst loadings often required to acquire XA-spectra with a satisfactory signal-to-noise ratio frequently imply the use of thick catalyst layers (CLs) with large ion- and mass-transport limitations. To shed light on the impact of this variable on the spectro-electrochemical results, in this study we investigate Pd-hydride formation in carbon-supported Pd-nanoparticles (Pd/C) and an unsupported Pd-aerogel with similar Pd surface areas but drastically different morphologies and electrode packing densities. Our in situ XAS and rotating disk electrode (RDE) measurements with different loadings unveil that the CL-thickness largely determines the hydride formation trends inferred from spectro-electrochemical experiments, therewith calling for the minimization of the CL-thickness in such experiments and the use of complementary thin-film control measurements.

A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation.

Background: Medical image segmentation is more complicated and demanding than ordinary image segmentation due to the density of medical pictures. A brain tumour is the most common cause of high mortality. Objectives: Extraction of tumorous cells is particularly difficult due to the differences between tumorous and non-tumorous cells. In ordinary convolutional neural networks, local background information is restricted. As a result, previous deep learning algorithms in medical imaging have struggled to detect anomalies in diverse cells. Methods: As a solution to this challenge, a deep convolutional generative adversarial network for tumour segmentation from brain Magnetic resonance Imaging (MRI) images is proposed. A generator and a discriminator are the two networks that make up the proposed model. This network focuses on tumour localisation, noise-related issues, and social class disparities. Results: Dice Score Coefficient (DSC), Peak Signal to Noise Ratio (PSNR), and Structural Index Similarity (SSIM) are all generally 0.894, 62.084 dB, and 0.88912, respectively. The model's accuracy has improved to 97 percent, and its loss has reduced to 0.012. Conclusions: Experiments reveal that the proposed approach may successfully segment tumorous and benign tissues. As a result, a novel brain tumour segmentation approach has been created.

Electrochemical Surface Area Quantification, CO2 Reduction Performance, and Stability Studies of Unsupported Three-Dimensional Au Aerogels versus Carbon-Supported Au Nanoparticles.

The efficient scale-up of CO2-reduction technologies is a pivotal step to facilitate intermittent energy storage and for closing the carbon cycle. However, there is a need to minimize the occurrence of undesirable side reactions like H2 evolution and achieve selective production of value-added CO2-reduction products (CO and HCOO-) at as-high-as-possible current densities. Employing novel electrocatalysts such as unsupported metal aerogels, which possess a highly porous three-dimensional nanostructure, offers a plausible approach to realize this. In this study, we first quantify the electrochemical surface area of an Au aerogel (≈5 nm in web thickness) using the surface oxide-reduction and copper underpotential deposition methods. Subsequently, the aerogel is tested for its CO2-reduction performance in an in-house developed, two-compartment electrochemical cell. For comparison purposes, similar measurements are also performed on polycrystalline Au and a commercial catalyst consisting of Au nanoparticles supported on carbon black (Au/C). The Au aerogel exhibits a faradaic efficiency of ≈97% for CO production at ≈-0.48 VRHE, with a suppression of H2 production compared to Au/C that we ascribe to its larger Au-particle size. Finally, identical-location transmission electron microscopy of both nanomaterials before and after CO2-reduction reveals that, unlike Au/C, the aerogel network retains its nanoarchitecture at the potential of peak CO production.