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Bank lending and environmental quality in Gulf Cooperation Council countries

To achieve economies with net-zero carbon emissions, it is essential to develop a robust green financial intermediary channel. This study seeks empirical evidence on how domestic bank lending to sovereign and private sectors in Gulf Cooperation Council (GCC) countries impacts carbon dioxide and greenhouse gas emissions. We employ PMG-ARDL model to panel data comprising six countries in GCC over twenty years for carbon dioxide emissions and nineteen years for greenhouse gas emissions. Our findings reveal a long-term positive impact of both bank lending variables on carbon dioxide and greenhouse gas emissions. In addition, lending to the government shows a negative short-term effect on greenhouse gas emissions. The cross-country results demonstrate the presence of a long-run effect of explanatory variables on both types of emissions, except for greenhouse gas in Saudi Arabia. The sort-term impact of the explanatory variables on carbon dioxide and greenhouse gas emissions is quite diverse. Not only do these effects differ across countries, but some variables have opposing effects on the two types of emissions within a single country. The findings of this study present a new perspective for GCC economies: neglecting total greenhouse gas emissions and concentrating solely on carbon dioxide emissions means missing critical information for devising effective strategies to combat threats of environmental degradation and achieve net-zero goals.

Hydrogen-bonded organic frameworks for photocatalytic synthesis of hydrogen peroxide

Photocatalysis provides a sustainable and environment-friendly strategy to produce H2O2, yet the catalytic efficiency of H2O2 overall photosynthesis (O2 + 2H2O → 2H2O2) needs to be further improved, especially in the absence of additional cocatalysts, photosensitizers and sacrificial agents. Here we find that hydrogen-bonded organic frameworks can serve as photocatalysts for H2O2 overall photosynthesis under the above-mentioned conditions. Specifically, we constructed a donor–acceptor hydrogen-bonded organic framework that exhibits a high photocatalytic activity for H2O2 overall photosynthesis, with a production rate of 681.2 μmol g-1 h-1. The control experiments and theoretical calculation revealed that the hydrogen-bonded organic frameworks with donor–acceptor structures can not only accelerate the charge separation and transfer but also optimize the reaction pathways, which significantly boosts the photocatalytic efficiency in H2O2 overall photosynthesis. This work provides insights into the design and development of efficient photocatalysts for overall H2O2 photosynthesis.

Photo-assisted technologies for environmental remediation

Industrial processes can lead to air and water pollution, particularly from organic contaminants such as toluene and antibiotics, posing threats to human health. Photo-assisted chemical oxidation technologies leverage light energy to mineralize these contaminants. In this Review, we discuss the mechanisms and efficiencies of photo-assisted advanced oxidation processes for wastewater treatment and photothermal technologies for air purification. The integration of solar energy enhances degradation efficiency and reduces energy consumption, enabling more efficient remediation methods. We evaluate the technological aspects of photo-assisted technologies, such as photo-Fenton, photo-persulfate activation, photo-ozonation and photoelectrochemical oxidation, emphasizing their potential for practical applications. Finally, we discuss the challenges in scaling up photo-assisted technologies for specific environmental remediation needs. Photo-assisted technologies have demonstrated effectiveness in environmental remediation, although large-scale applications remain constrained by high costs. Future potential applications of photo-assisted technologies will require that technology selection be tailored to specific pollution scenarios and engineering processes optimized to minimize costs.

Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts

Adsorbates often cover the surfaces of catalysts densely as they carry out reactions, dynamically altering their structure and reactivity. Understanding adsorbate-induced phenomena and harnessing them in our broader quest for improved catalysts is a substantial challenge that is only beginning to be addressed. Here we chart a path toward a deeper understanding of such phenomena by focusing on emerging in silico modeling methodologies, which will increasingly incorporate machine learning techniques. We first examine how adsorption on catalyst surfaces can lead to local and even global structural changes spanning entire nanoparticles, and how this affects their reactivity. We then evaluate current efforts and the remaining challenges in developing robust and predictive simulations for modeling such behavior. Last, we provide our perspectives in four critical areas—integration of artificial intelligence, building robust catalysis informatics infrastructure, synergism with experimental characterization, and adaptive modeling frameworks—that we believe can help surmount the remaining challenges in rationally designing catalysts in light of these complex phenomena.

The design space of E(3)-equivariant atom-centred interatomic potentials

Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets.

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