MnO2/TiO2-Catalyzed ozonolysis: enhancing Pentachlorophenol degradation and understanding intermediates.

Pentachlorophenol is a pesticide widely known for its harmful effects on sewage, causing harm to the environment. In previous studies, our group identified adsorption as a crucial factor in catalytic ozonation processes, and subsequent observations revealed the catalyst's role in reducing toxicity during degradation. In this research, we quantified organochlorine intermediates and low molecular weight organic acids generated under optimal pH conditions (pH 9), with and without the catalyst. Additionally, we assessed the reactivity of these intermediates through theoretical calculations. Our findings indicate that the catalyst reduces the duration of intermediates. Additionally, the presence of CO2 suggests enhanced mineralization of pentachlorophenol, a process notably facilitated by the catalyst. Theoretical calculations, such as Fukui analysis, offer insights into potential pathways for the dechlorination of aromatic molecules by radicals like OH, indicating the significance of this pathway.

Machine Learning Identification of Organic Compounds Using Visible Light.

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols and applications.

Anisotropic Band-Edge Absorption of Millimeter-Sized Zn(3-ptz)2 Single-Crystal Metal-Organic Frameworks.

Metal-organic frameworks (MOFs) have emerged as promising tailor-designed materials for developing next-generation solid-state devices with applications in linear and nonlinear coherent optics. However, the implementation of functional devices is challenged by the notoriously difficult process of growing large MOF single crystals of high optical quality. By controlling the solvothermal synthesis conditions, we succeeded in producing large individual single crystals of the noncentrosymmetric MOF Zn(3-ptz)2 (MIRO-101) with a deformed octahedron habit and surface areas of up to 37 mm2. We measured the UV-vis absorption spectrum of individual Zn(3-ptz)2 single crystals across different lateral incidence planes. Millimeter-sized single crystals have a band gap of E g = 3.32 eV and exhibit anisotropic absorption in the band-edge region near 350 nm, whereas polycrystalline samples are fully transparent in the same frequency range. Using solid-state density functional theory (DFT), the observed size dependence in the optical anisotropy is correlated with the preferred orientation adopted by pyridyl groups under conditions of slow crystal self-assembly. Our work thus paves the way for the development of optical polarization switches based on metal-organic frameworks.

Engineering entangled photon pairs with metal-organic frameworks.

The discovery and design of new materials with competitive optical frequency conversion efficiencies can accelerate the development of scalable photonic quantum technologies. Metal-organic framework (MOF) crystals without inversion symmetry have shown potential for these applications, given their nonlinear optical properties and the combinatorial number of possibilities for MOF self-assembly. In order to accelerate the discovery of MOF materials for quantum optical technologies, scalable computational assessment tools are needed. We develop a multi-scale methodology to study the wavefunction of entangled photon pairs generated by selected non-centrosymmetric MOF crystals via spontaneous parametric down-conversion (SPDC). Starting from an optimized crystal structure, we predict the shape of the G (2) intensity correlation function for coincidence detection of the entangled pairs, produced under conditions of collinear type-I phase matching. The effective nonlinearities and photon pair correlation times obtained are comparable to those available with inorganic crystal standards. Our work thus provides fundamental insights into the structure-property relationships for entangled photon generation with metal-organic frameworks, paving the way for the automated discovery of molecular materials for optical quantum technology.

Multiscale Simulations of Clavulanate Inhibition Identify the Reactive Complex in Class A ß-Lactamases and Predict the Efficiency of Inhibition.

Clavulanate is used as an effective drug in combination with ß-lactam antibiotics to treat infections of some antibiotic resistant bacteria. Here, we perform combined quantum mechanics/molecular mechanics simulations of several covalent complexes of clavulanate with class A ß-lactamases KPC-2 and TEM-1. Simulations of the deacylation reactions identify the decarboxylated trans-enamine complex as being responsible for inhibition. Further, the obtained free energy barriers discriminate clinically relevant inhibition (TEM-1) from less effective inhibition (KPC-2).