A new molecule, 1,3,6,8-tetramethylpyrene (TMPY), with a similar shape to the luminescent material perylene has been successfully synthesized. The co-crystals with perylene doping have been grown and their crystal structure has been clarified by X-ray analysis. Optical spectra indicate that effective energy transfer has been achieved in the doping systems and the luminescence efficiency has reached as high as 78%. The pure TMPY shows p-type characteristics during the FET operation. The hole mobility is up to 0.26 cm 2 V À1 s À1 .

hindered because the corresponding reaction requires only two electrons and two protons, making it a promising initial feedstock. [19] Recent studies have described high (>90%) Faradaic efficiency (FE) for CO 2 RR to CO at high current density (>50 mA cm −2) for several different catalysts. [20-23] Among catalysts, metaldoped carbon materials have emerged at the intersection between heterogeneous carbon and metal microcycles, showing high selectivity toward CO 2 RR to CO. [23-38] Despite the wide variety of structural designs, most catalysts were shown to promote CO 2 RR only in CO 2-saturated basic or neutral aqueous solution to suppress the competing hydrogen evolution reaction (HER). Another underlying challenge is that the synthetic procedures often require relatively complicated steps such as templating. Such constraints impose practical difficulties and additional costs for operation. As seen in fuel-cell technology, a membrane electrode assembly (MEA) offers technical advantages in terms of scalability, design flexibility, and ease of operation. [39] However, only a few studies have demonstrated CO 2 RR with MEAs with either cation-exchange [25,36,40,41] or anion-exchange membranes. [37,42,43] While cation-exchange based MEAs face a fundamental challenge due to the competing HER because of the acidic environment, they have great potential for commercialization because of their highly durable and widely available components. [44] Herein, we describe a straightforward one-pot synthesis of cobalt and organic [poly-4-vinylpyridine (P4VP)] precursors with carbon supports to form the cathode of a highly effective nafion-based MEA, using the synergy between the reduced cobalt and pyridine moieties to drive the CO 2 RR. Studies indicate that the catalyst performed CO 2 RR predominantly over HER across a wide range of pH. Optimization of the catalyst components led to CO production with 92% FE and 58% EE at 85 mA cm −2 , and preliminary durability tests showed stable FE for 20 h of operation. Metal-pyridine derived carbon catalysts were synthesized according to the reported pyrolysis method as illustrated in Figure 1a. [25,36] Metal (Co, Fe, and Ni) nitrate and pyridine derivatives [4-ethylpyridine (EPy), 4-aminopyridine (APy), poly-4-vinylpyridine (P4VP), and poly-2-vinylpyridine (P2VP)], There is great need for the development of an electrochemical CO 2 reduction reaction (CO 2 RR) process with high Faraday efficiency (FE), energy efficiency (EE), and current density for practical utilization of CO 2. Here, a facile one-pot synthesis of a catalyst is reported that is based on cobalt and poly-4-vinylpyridine that can perform CO 2 RR to CO predominantly with respect to the hydrogen evolution reaction in a nafion-based membrane electrode assembly and can work in pH ranging from 2 to 7. Cell optimization results in CO 2 RR to CO with 92% FE and 58% EE at 85 mA cm −2 , while showing no noticeable degradation in FE at 20 h. These characteristics are attributed to synthesis and processing conditions which promote nearly...

Applied machine learning has rapidly spread throughout the physical sciences. In fact, machine learning-based data analysis and experimental decision-making have become commonplace. Here, we reflect on the ongoing shift in the conversation from proving that machine learning can be used, to how to effectively implement it for advancing materials science. In particular, we advocate a shift from a big data and large-scale computations mentality to a model-oriented approach that prioritizes the use of machine learning to support the ecosystem of computational models and experimental measurements. We also recommend an open conversation about dataset bias to stabilize productive research through careful model interrogation and deliberate exploitation of known biases. Further, we encourage the community to develop machine learning methods that connect experiments with theoretical models to increase scientific understanding rather than incrementally optimizing materials. Moreover, we envision a future of radical materials innovations enabled by computational creativity tools combined with online visualization and analysis tools that support active outside-the-box thinking within the scientific knowledge feedback loop.