Hydrothermal polymerization of porous aromatic polyimide networks and machine learning-assisted computational morphology evolution interpretation

Green synthesis of bifunctional phthalocyanine-porphyrin cofs in water for efficient electrocatalytic CO2 reduction coupled with methanol oxidation

Electrocatalytic CO2 reduction (ECR) coupled with organic oxidation is a promising strategy to produce high value-added chemicals and improve energy efficiency. However, achieving the efficient redox coupling reaction is still challenging due to the lack of suitable electrocatalysts. Herein, we designed two bifunctional polyimides-linked covalent organic frameworks (PI-COFs) through assembling phthalocyanine (Pc) and porphyrin (Por) by non-toxic hydrothermal methods in pure water to realize the above catalytic reactions. Due to the high conductivity and well-defined active sites with different chemical environments, NiPc-NiPor COF performs efficient ECR coupled with methanol oxidation reaction (MOR) (Faradaic efficiency of CO (FECO) = 98.12%, partial current densities of CO (jCO) = 6.14 mA cm-2 for ECR, FEHCOOH = 93.75%, jHCOOH = 5.81 mA cm-2 for MOR at low cell voltage (2.1 V) and remarkable long-term stability). Furthermore, experimental evidences and density functional theory (DFT) calculations demonstrate that the ECR process mainly conducts on NiPc unit with the assistance of NiPor, meanwhile, the MOR prefers NiPor conjugating with NiPc. The two units of NiPc-NiPor COF collaboratively promote the coupled oxidation-reduction reaction. For the first time, this work achieves the rational design of bifunctional COFs for coupled heterogeneous catalysis, which opens a new area for crystalline material catalysts.

Urethane functions can reduce metal salts under hydrothermal conditions: synthesis of noble metal nanoparticles on flexible sponges applied in semi-automated organic reduction

We report an additive-free one-pot hydrothermal synthesis of Au, Ag, Pd, and alloy AuPd nanoparticles (NPs) anchored on commercial polyurethane (PU) foams. While unable to reduce the precursor metal salts at room temperature, PU is able to serve as a reducing agent under hydrothermal conditions. The resulting NP@PU sponge materials perform comparably to reported state-of-the-art reduction catalysts, and are additionally very well suited for use in semi-automated synthesis: the NP anchoring is strong enough and the support flexible enough to be used as a 'catalytic sponge' that can be manipulated with a robotic arm, i.e., be repeatedly dipped into and drawn out of solutions, wrung out, and re-soaked.

Dual redox-active porous polyimides as high performance and versatile electrode material for next-generation batteries

Energy storage will be a primordial actor of the ecological transition initiated in the energy and transport sectors. As such, innovative approaches to design high-performance electrode materials are crucial for the development of the next generation of batteries. Herein, a novel dual redox-active and porous polyimide network (MTA-MPT), based on mellitic trianhydride (MTA) and 3,7-diamino-N-methylphenothiazine (MPT) monomers, is proposed for applications in both high energy density lithium batteries and symmetric all-organic batteries. The MTA-MPT porous polyimide was synthesized using a novel environmentally-friendly hydrothermal polymerization method. Rooted in its dual redox proprieties, the MTA-MPT porous polyimide exhibits a high theoretical capacity making it a very attractive cathode material for high energy density battery applications. The cycling performance of this novel electrode material was assessed in both high energy density lithium batteries and light-weight symmetric all-organic batteries, displaying excellent rate capability and long-term cycling stability.

Porous organic polymers for CO2 capture, separation and conversion

Porous organic polymers (POPs) have long been considered as prime candidates for carbon dioxide (CO2) capture, separation, and conversion. Especially their permanent porosity, structural tunability, stability and relatively low cost are key factors in such considerations. Whereas heteratom-rich microporous networks as well as their amine impregnation/functionalization have been actively exploited to boost the CO2 affinity of POPs, recently, the focus has shifted to engineering the pore environment, resulting in a new generation of highly microporous POPs rich in heteroatoms and featuring abundant catalytic sites for the capture and conversion of CO2 into value-added products. In this review, we aim to provide key insights into structure-property relationships governing the separation, capture and conversion of CO2 using POPs and highlight recent advances in the field.

Automated Design of Li+ -Conducting Polymer by Quantum-Inspired Annealing

Automated molecule design by computers has been an essential topic in materials informatics. Still, generating practical structures is not easy because of the difficulty in treating material stability, synthetic difficulty, mechanical properties, and other miscellaneous parameters, often leading to the generation of junk molecules. We tackle the problem by introducing supervised/unsupervised machine learning and quantum-inspired annealing. Our autonomous molecular design system can help experimental researchers discover practical materials more efficiently. Like the human design process, new molecules are explored based on knowledge of existing compounds. A new solid-state polymer electrolyte for lithium-ion batteries is designed and synthesized, giving a promising room temperature conductivity of 10^-5 S/cm with reasonable thermal, chemical, and mechanical properties. This article is protected by copyright. All rights reserved.

Use of Artificial Intelligence Methods for Predicting the Strength of Recycled Aggregate Concrete and the Influence of Raw Ingredients

Cracking is one of the main problems in concrete structures and is affected by various parameters. The step-by-step laboratory method, which includes casting specimens, curing for a certain period, and testing, remains a source of worry in terms of cost and time. Novel machine learning methods for anticipating the behavior of raw materials on the ultimate output of concrete are being introduced to address the difficulties outlined above such as the excessive consumption of time and money. This work estimates the splitting-tensile strength of concrete containing recycled coarse aggregate (RCA) using artificial intelligence methods considering nine input parameters and 154 mixes. One individual machine learning algorithm (support vector machine) and three ensembled machine learning algorithms (AdaBoost, Bagging, and random forest) are considered. Additionally, a post hoc model-agnostic method named SHapley Additive exPlanations (SHAP) was performed to study the influence of raw ingredients on the splitting-tensile strength. The model's performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Then, the model's performance was validated using k-fold cross-validation. The random forest model, with an R² of 0.96, outperformed the AdaBoost models. The random forest models with greater R² and lower error (RMSE = 0.49) had superior performance. It was revealed from the SHAP analysis that the cement content had the highest positive influence on the splitting-tensile strength of the recycled aggregate concrete and the primary contact of cement is with water. The feature interaction plot shows that high water content has a negative impact on the recycled aggregate concrete (RAC) splitting-tensile strength, but the increased cement content had a beneficial effect.