Optimizing Photocatalytic H2 Production by Introduction of Pyrazinyls to WRCs and a New tris-Rhenium Photosensitizer.

The replacement of pyridyl by pyrazinyl in ligands of polypyridyl-based cobalt water reducing catalysts (WRC) shifts reduction potentials anodically. Together with a new, trinuclear ReI photosensitizer, these WRCs show strongly improved photocatalytic performances in turnover numbers (TONs) and maximal H2 evolution rate. Depending on the catalyst structure, up to 65 kTONs at 1 µM WRC concentration were reached. Under electrocatalytic conditions in both DMF and H2O, one of the reported WRCs displays remarkable stability, producing H2 steadily over 21 and 14 d, respectively.

Predicting survival of Iranian COVID-19 patients infected by various variants including omicron from CT Scan images and clinical data using deep neural networks.

Purpose: The rapid spread of the COVID-19 omicron variant virus has resulted in an overload of hospitals around the globe. As a result, many patients are deprived of hospital facilities, increasing mortality rates. Therefore, mortality rates can be reduced by efficiently assigning facilities to higher-risk patients. Therefore, it is crucial to estimate patients' survival probability based on their conditions at the time of admission so that the minimum required facilities can be provided, allowing more opportunities to be available for those who need them. Although radiologic findings in chest computerized tomography scans show various patterns, considering the individual risk factors and other underlying diseases, it is difficult to predict patient prognosis through routine clinical or statistical analysis. Method: In this study, a deep neural network model is proposed for predicting survival based on simple clinical features, blood tests, axial computerized tomography scan images of lungs, and the patients' planned treatment. The model's architecture combines a Convolutional Neural Network and a Long Short Term Memory network. The model was trained using 390 survivors and 108 deceased patients from the Rasoul Akram Hospital and evaluated 109 surviving and 36 deceased patients infected by the omicron variant. Results: The proposed model reached an accuracy of 87.5% on the test data, indicating survival prediction possibility. The accuracy was significantly higher than the accuracy achieved by classical machine learning methods without considering computerized tomography scan images (p-value <= 4E-5). The images were also replaced with hand-crafted features related to the ratio of infected lung lobes used in classical machine-learning models. The highest-performing model reached an accuracy of 84.5%, which was considerably higher than the models trained on mere clinical information (p-value <= 0.006). However, the performance was still significantly less than the deep model (p-value <= 0.016). Conclusion: The proposed deep model achieved a higher accuracy than classical machine learning methods trained on features other than computerized tomography scan images. This proves the images contain extra information. Meanwhile, Artificial Intelligence methods with multimodal inputs can be more reliable and accurate than computerized tomography severity scores.

Green carbon-carbon homocoupling of terminal alkynes by a silica supported Cu(II)-hydrazone coordination compound.

A Cu(II) complex, [Cu(HL)(NO3)(CH3OH)]·CH3OH (1), was obtained by the reaction of Cu(NO3)2·3H2O and H2L in methanol solvent (H2L is (E)-4-amino-N'-(2-hydroxy-3-methoxybenzylidene)benzohydrazide). H2L and compound 1 were characterized by various spectroscopic analyses and the molecular structure of [Cu(HL)(NO3)(CH3OH)]·CH3OH was determined by single-crystal X-ray analysis. The results indicated the product is a mononuclear Cu(II) complex and contains a free NH2 functional group on the structure of the ligand. [Cu(HL)(NO3)(CH3OH)]·CH3OH was used for the preparation of a heterogeneous catalyst by supporting it on functionalized silica gel. The heterogeneous catalyst (Si-Cu) was prepared by an amidification reaction of [Cu(HL)(NO3)(CH3OH)]·CH3OH with functionalized silica gel. The resulting silica-supported catalyst (Si-Cu) was characterized by TGA, FT-IR, EPR, DRS, EDS, XRD, SEM and XPS analyses. Si-Cu was employed in a carbon-carbon coupling reaction and the effects of the amount of Si-Cu and temperature were investigated in the catalytic coupling. The structure of one of the products of the catalytic reactions (C16H22O2, CP1) was determined by single-crystal X-ray analysis, which proved the formation of a C-C bond and the production of di-acetylene by homocoupling of terminal alkyne. This catalytic system is stable and it can be reused for a coupling reaction without a significant change in its catalytic activity.

Water oxidation reaction in the presence of a dinuclear Mn(II)-semicarbohydrazone coordination compound.

Water splitting, producing of oxygen, and hydrogen molecules, is an essential reaction for clean energy resources and is one of the challenging reactions for artificial photosynthesis. The Mn4Ca cluster in photosystem II (PS-II) is responsible for water oxidation in natural photosynthesis. Due to this, water oxidation reaction by Mn coordination compounds is vital for mimicking the active core of the oxygen-evolving complex in PS-II. Here, a new dinuclear Mn(II)-semicarbohydrazone coordination compound, [Mn(HL)(µ-N3)Cl]2 (1), was synthesized and characterized by various methods. The structure of compound 1 was determined by single crystal X-ray analysis, which revealed the Mn(II) ions have distorted octahedral geometry as (MnN4OCl). This geometry is created by coordinating of oxygen and two nitrogen donor atoms from semicarbohydrazone ligand, two nitrogen atoms from azide bridges, and chloride anion. Compound 1 was used as a catalyst for electrochemical water oxidation, and the surface of the electrode after the reaction was investigated by scanning electron microscopy, energy dispersive spectrometry, and powder X-ray diffraction analyses. Linear sweep voltammetry (LSV) experiments revealed that the electrode containing 1 shows high activity for chemical water oxidation with an electrochemical overpotential as low as 377 mV. Although our findings showed that the carbon paste electrode in the presence of 1 is an efficient electrode for water oxidation, it could not withstand water oxidation catalysis under bulk electrolysis and finally converted to Mn oxide nanoparticles which were active for water oxidation along with compound 1.

Green solvent free epoxidation of olefins by a heterogenised hydrazone-dioxidotungsten(vi) coordination compound.

A new mononuclear tungsten coordination compound, [WO2L(CH3OH)] (1), was synthesized by the reaction of WCl6 and H2L (H2L = (E)-4-amino-N'-(5-bromo-2-hydroxybenzylidene)benzohydrazide) in methanol. Both the H2L and compound 1 were characterized by elemental analysis and UV-Vis, FT-IR and NMR spectroscopic methods. The molecular structure of compound 1 was also determined by single crystal X-ray analysis which confirmed the compound is a mononuclear coordination compound of cis-dioxidotungsten(vi) containing a free amine functionality on the ligand. Compound 1 was supported on propionyl chloride-functionalized silica gel by amidification reaction to obtain a heterogeneous catalyst. The obtained heterogeneous catalyst was characterized by FT-IR spectroscopy, thermal gravimetric analysis (TGA), diffuse-reflectance spectroscopy (DRS), X-ray diffraction analysis (XRD), energy-dispersive X-ray spectroscopy (EDX), X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM) and its catalytic activity was investigated in the epoxidation of olefins with hydrogen peroxide under solvent free conditions. The catalyst was successfully recovered several times and the recovered catalyst was also characterized by various methods including FT-IR, DRS, TGA, SEM and EDX analyses. The results indicated this heterogeneous catalytic system is an effective and selective catalyst for epoxidation of olefins and can be reused several times without significant change in its catalytic activity.