Charge Photoaccumulation in Covalent Polymer Networks for Boosting Photocatalytic Nitrate Reduction to Ammonia.

In the design of photoelectrocatalytic cells, a key element is effective photogeneration of electron-hole pairs to drive redox activation of catalysts. Despite recent progress in photoelectrocatalysis, experimental realization of a high-performance photocathode for multi-electron reduction of chemicals, such as nitrate reduction to ammonia, has remained a challenge due to difficulty in obtaining efficient electrode configurations for extraction of high-throughput electrons from absorbed photons. This work describes a new design for catalytic photoelectrodes using chromophore assembly-functionalized covalent networks for boosting eight-electron reduction of nitrate to ammonia. Upon sunlight irradiation, the photoelectrode stores a mass of reducing equivalents at the photoexcited chromophore assembly for multielectron reduction of a copper catalyst, enabling efficient nitrate reduction to ammonia. By introducing the new photoelectrode structure, it is demonstrated that the electronic interplay between charge photo-accumulating assembly and multi-electron redox catalysts can be optimized to achieve proper balance between electron transfer dynamics and thermodynamic output of photoelectrocatalytic systems.

Molecular Wiring of Electrocatalytic Nitrate reduction to Ammonia and Water Oxidation by Iron-Coordinated Macroporous Conductive Networks.

Developing stable electrocatalysts with accessible isolated sites is desirable but highly challenging due to metal agglomeration and low surface stability of host materials. Here we report a general approach for synthesis of single-site Fe electrocatalysts by integrating a solvated Fe complex in conductive macroporous organic networks through redox-active coordination linkages. Electrochemical activation of the electrode exposes high-density coordinately unsaturated Fe sites for efficient adsorption and conversion of reaction substrates such as NO3 - and H2O. Using the electrode with isolated active Fe sites, electrocatalytic NO3 - reduction and H2O oxidation can be coupled in a single cell to produce NH3 and O2 at Faradaic efficiencies of 97 % and 100 %, respectively. The electrode exhibits excellent robustness in electrocatalysis for 200 hours with small decrease in catalytic efficiencies. Both the maximized Fe-site efficiency and the microscopic localization effect of the conductive organic matrix contribute to the high catalytic performances, which provides new understandings in tuning the efficiencies of metal catalysts for high-performance electrocatalytic cells.

Enhanced 3-D asynchronous correlation data preprocessing method for Raman spectroscopy of Chinese handmade paper.

We have developed a novel 3D asynchronous correlation method (3D-ACM) designed for the classification and identification of Chinese handmade paper samples using Raman spectra and machine learning. The 3D-ACM approach involves two rounds of tensor product and Hilbert transform operations. In the tensor product process, the outer product of the spectral data from different samples within the same category is computed, establishing inner connections among all samples within that category. The Hilbert transform introduces a 90-degree phase shift, resulting in a true three-dimensional spectral data structure. This expansion significantly increases the number of equivalent frequency points and samples within each category. This enhancement substantially boosts spectral resolution and reveals more hidden information within the spectral data. To maximize the potential of 3D-ACM, we employed six machine learning models: principal component analysis (PCA) with linear regression (LR), support vector machine (SVM) with LR, k-Nearest Neighbors (KNN), random forest (RF), and convolutional neural network (CNN). When applied to the 3D-ACM data preprocessing method, R-squared values of PLS-LR, KNN, RF and CNN supervised models, approached or equaled 1. This indicates exceptional performance comparable to unsupervised models like PCA. 3D-ACM stands as a versatile mathematical technique not confined to spectral data. It also eliminates the necessity for additional experimental setups or external control conditions, distinct from traditional two-dimensional correlation spectroscopy. Moreover, it preserves the original experimental data, setting it apart from conventional data preprocessing methods. This positions 3D-ACM as a promising tool for future material classification and identification in conjunction with machine learning.

Molecular Self-Assembly in Conductive Covalent Networks for Selective Nitrate Electroreduction to Ammonia.

Electrochemical nitrate (NO3-) reduction in aqueous media provides a useful approach for ammonia (NH3) synthesis. While efforts are focused on developing catalysts, the local microenvironment surrounding the catalyst centers is of great importance for controlling electrocatalytic performance. Here, we demonstrate that a self-assembled molecular iron catalyst integrated in a free-standing conductive hydrogel is capable of selective production of NH3 from NO3- at efficiencies approaching unity. With the electrocatalytic hydrogel, the NH3 selectivity is consistently high under a range of negative biases, which results from the hydrophobicity increase of the polycarbazole-based electrode substrate. In mildly acidic media, proton reduction is suppressed by electro-dewetting of the hydrogel electrode, enhancing the selectivity of NO3- reduction. The electrocatalytic hydrogel is capable of continuous production of NH3 for at least 100 h with NH3 selectivity of ∼89 to 98% at high current densities. Our results highlight the role of constructing an internal hydrophobic surface for electrocatalysts in controlling selectivity in aqueous media.