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Language: search.filters.language.EnglishSubject: search.filters.subject.Carbon dioxide

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    Item type:Publication,
    Cycloaddition of CO
    <sub>2</sub>
    and Epoxide at Ambient Conditions Catalyzed by PW
    <sub>12</sub>
    @HKUST-1 Composite and Optimization Study Using RSM
    (American Chemical Society (ACS), 2026-02-06)
    Suleiman Gani Musa
    ;
    Zulkifli Merican Aljunid Merican
    ;
    Abdurrashid Haruna
    ;
    Noor Asmawati Binti Mohd Zabidi
    ;
    Mohammad Yusuf
    The search for a sustainable and effective catalyst for CO2 fixing using epoxides is part of a global quest for economical carbon capture and utilization solutions. The coupling of metal–organic frameworks (MOFs) with other functional nanomaterials such as polyoxometalates (POMs) has proven to be effective in increasing the heterogeneity and stability of pristine MOF materials. We demonstrated the application of MOF/POM-supported composites (POM@MOF) as catalysts for the fixation of CO2 and epichlorohydrin epoxide (ECH) to chloropropene carbonate. The catalyst was synthesized by impregnating HKUST-1 with (TBA)3PW12O40, a Keggin-type polyoxometalate. The obtained composite, PW12@HKUST-1, was characterized by Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy-dispersive spectroscopy (EDS), and N2 adsorption–desorption isotherms. The POM’s presence was confirmed by various analyses. Optimization of the reaction variables was conducted using the response surface methodology model. The optimum condition for the CCD-RSM studies was catalyst amount: 12.50 mg, cocatalyst amount: 0.055 mmol, temperature: 100 °C, and time: mechanism of CO2 conversion 15 h, attaining 89.70% conversion and 97% selectivity. The catalyst shows a remarkable increase in stability and reusability by recycling six times in a row without any significant decrease in catalytic activity.
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    Photocatalytic CO2 conversions on copper nanoparticles investigated by Raman spectral changes using convolutional neural networks
    (Elsevier BV, 2025-10)
    Heung Seok Lee
    ;
    Jaerin Choi
    ;
    Jin Yong Lee
    ;
    Ji Eun An
    ;
    Thi Huong Vu
    A convolutional neural network (CNN) deep learning process is employed to analyze in situ Raman scattering data for CO2 capture and its photocatalytic conversions onto copper sulfide hollow nanospheres (CuSHNSs) and copper nanocubes (CuNCs) in microalgae solution of Spirulina maxima. Raman spectra under visible light at 633 nm in a microfluidic solution provided representative vibrational marker bands of Cdouble bondO features at ∼2100 cm−1 and CH2/CH3 bending vibrations at ∼1400 cm−1 that are correlated with CO2 reduction products of carbon monoxide (C1) and multi‑carbon species such as propanol (C3), butanol (C4), respectively. Accumulated Raman spectra were trained and analyzed to estimate photocatalytic pathways using CNN algorithm. The presence of Spirulina maxima microalgae on the alteration of photocatalytic processes is studied by analyzing collective Raman spectral changes. The main observation is that strong CO peaks in Raman spectra of CO2 adsorbed by CuNCs almost disappeared after treatment with microalgae, whereas their intensities were slightly increased in case of CuSHNS. The CNN deep learning process for Raman spectra was effective to differentiate photocatalytic mechanisms of CO2 conversion onto nanoparticle surfaces.