Oscar Díaz

In this work, the photocatalytic reduction of CO₂ was innovatively tested with the simultaneous removal and mineralization of a textile contaminant, methylene blue (MB), which acts as a sacrificial agent. The process was carried out in a flow regime under atmospheric conditions, using a liquid-phase photoreactor under UVA illumination with a duration of 24 h per test. Two commercial TiO₂-based photocatalysts, P25 and P90 from Evonik, were used and surface modified through the photodeposition of metallic nanoparticles of Pt, Au, and Pd, as they did not show gas-phase products from CO₂ reduction on their own. The optimal pH was 5, the decreasing order of activity by metal was Pt > Au > Pd, and the optimal MB concentration was 20 ppm. The major products were CH₄ and H₂ in the gas phase. The presence of CH₄ was only detected in the presence of a CO₂ flow. In the liquid phase, carboxylic acids were also detected in small amounts, and in the test, 100 ppm of MB ethanol was additionally detected. A 100% degradation of MB and 72.5% mineralization was achieved under the conditions of highest CH₄ production (20 ppm MB at pH 5 with 4 g·L⁻¹ P25-0.70%Pt).

The increasing volume of research paper submissions poses a significant challenge to the traditional academic peer-review system, leading to an overwhelming workload for reviewers. This study explores the potential of integrating Large Language Models (LLMs) into the peer-review process to enhance efficiency without compromising effectiveness. We focus on manuscript annotations, particularly excerpt highlights, as a potential area for AI-human collaboration. While LLMs excel in certain tasks like aspect coverage and informativeness, they often lack high-level analysis and critical thinking, making them unsuitable for replacing human reviewers entirely. Our approach involves using LLMs to assist with specific aspects of the review process. This paper introduces AnnotateGPT, a platform that utilizes GPT-4 for manuscript review, aiming to improve reviewers' comprehension and focus. We evaluate AnnotateGPT using a Technology Acceptance Model (TAM) questionnaire with nine participants and generalize the findings. Our work highlights annotation as a viable middle ground for AI-human collaboration in academic review, offering insights into integrating LLMs into the review process and tuning traditional annotation tools for LLM incorporation.