High surface area siloxene for photothermal and electrochemical catalysis

Cu-Pd bimetal-decorated siloxene nanosheets for semi-hydrogenation of acetylene

Metallic Pd has been proved highly promising when paired with Cu for industrially important acetylene semi-hydrogenation. Herein, we demonstrate that high-surface-area siloxene can feasibly enable alloying between Pd and Cu via room-temperature reduction with Si-H bonds. Unprecedentedly small Cu nanoparticles with isolated Pd were in situ loaded on siloxene, addressing the core problem of low selectivity of Pd and low activity of Cu. This devised structure outclassed the traditional impregnated SiO2 in every aspect of the catalytic performance for the semi-hydrogenation of acetylene under industry conditions, with a 91% acetylene conversion and an impressive 93% selectivity to ethylene at 200 °C, and showed long-term stability with negligible activity decay at this harsh temperature. This work provides new insights for the design of economic bimetallic loaded catalysts for balancing the activity-selectivity dilemma, demonstrating the viability of siloxene as both a synthetic reagent and a carrier material for efficient catalysis.

Photocatalytic degradation of drugs and dyes using a maching learning approach

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior of organic pollutants during catalytic degradation. With the increasing quantity of waste generated, these models are critical for reinforcing the efficiency of wastewater treatment strategies. The application of machine-learning techniques in recent years has notably improved predictive models for waste management, which are essential for mitigating the impact of toxic commercial waste on global water supply. Organic contaminants, dyes, pesticides, surfactants, petroleum by-products, and prescription drugs pose risks to human health. Because traditional techniques face challenges in ensuring water quality, modern strategies are vital. Machine learning has emerged as a valuable tool for predicting the photocatalytic degradation of medicinal drugs and dyes, providing a promising avenue for addressing urgent demands in removing organic pollutants from wastewater. This research investigates the synergistic application of photocatalysis and machine learning for pollutant degradation, showcasing a sustainable solution with promising effects on environmental remediation and computational efficiency. This study contributes to green chemistry by providing a clever framework for addressing present-day water pollution challenges and achieving era-driven answers.

A nonmetallic plasmonic catalyst for photothermal CO2 flow conversion with high activity, selectivity and durability

The meticulous design of active sites and light absorbers holds the key to the development of high-performance photothermal catalysts for CO2 hydrogenation. Here, we report a nonmetallic plasmonic catalyst of Mo2N/MoO2-x nanosheets by integrating a localized surface plasmon resonance effect with two distinct types of active sites for CO2 hydrogenation. Leveraging the synergism of dual active sites, H2 and CO2 molecules can be simultaneously adsorbed and activated on N atom and O vacancy, respectively. Meanwhile, the plasmonic effect of this noble-metal-free catalyst signifies its promising ability to convert photon energy into localized heat. Consequently, Mo2N/MoO2-x nanosheets exhibit remarkable photothermal catalytic performance in reverse water-gas shift reaction. Under continuous full-spectrum light irradiation (3 W·cm-2) for a duration of 168 h, the nanosheets achieve a CO yield rate of 355 mmol·gcat-1·h-1 in a flow reactor with a selectivity exceeding 99%. This work offers valuable insights into the precise design of noble-metal-free active sites and the development of plasmonic catalysts for reducing carbon footprints.

Bifunctional Solar Evaporator with Co/N-Doped Graphene Oxide for Synergistic Steam Generation and Organic Pollutant Degradation

Solar-driven interfacial steam generation (SISG) is a promising technology for alleviating freshwater shortage. However, when the SISG technology is applied to wastewater treatment, the contaminant would be enriched in residual bulk water. Herein, a dual-functional evaporator was constructed via tactfully decorating Co/N-doped graphene oxide (GO) on melamine foam (MF), which can simultaneously achieve efficient vapor production and source water purification. N-doped carbon nanotubes (NCNTs) endowed evaporators with powerful light absorption and water transport performance, guaranteeing an evaporation rate of 2.02 kg m-2 h-1 under 1 sun irradiation. Meanwhile, the catalytic activity of the carbon layer was adjusted by the N dopant and embedded Co particles, providing abundant active sites to activate peroxymonosulfate (PMS). When treating the solution containing sulfamethoxazole (SMX), no SMX residues were detected in the remaining bulk water (up to 100% SMX degradation efficiency within 60 min), demonstrating that reactive oxygen species (ROS) were generated to attack SMX in the source water. The bifunctional evaporator successfully combined SISG and advanced oxidation processes (AOPs), providing an ingenious strategy for solving the problem of wastewater enrichment during SISG.

Two-Dimensional Siloxene Nanosheets: Impact of Morphology and Purity on Electrochemistry

Two-dimensional (2D) siloxene (Si₆O₃H₆) has shown promise as a negative electrode material for Li-ion batteries due to its high gravimetric capacity and superior mechanical properties under (de)lithiation compared to bulk Si. In this work, we prepare purified siloxene nanosheets through the removal of bulk Si contaminants, use ultrasonication to control the lateral size and thickness of the nanosheets, and probe the effects of the resulting morphology and purity on the electrochemistry. The thin siloxene nanosheets formed after 4 h of ultrasonication deliver an average capacity of 810 mA h/g under a 1000 mA/g rate over 200 cycles with a capacity retention of 76%. Interestingly, the purified siloxene shows lower initial capacity but superior capacity retention over extended cycling. The 2D morphology benefit is illustrated where the parent siloxene nanosheet morphology and structure were largely maintained based on operando optoelectrochemistry, in situ Raman, ex situ scanning electron microscopy, and ex situ transmission electron microscopy. Furthermore, the purified siloxene-based electrode free from crystalline Si impurity experiences the least expansion upon (de)lithiation as visualized by cross-section electron microscopy of samples recovered post-cycling.