Kan Hatakeyama‐Sato 교수

Charge transport processes in nonconjugated redox-active polymers with electrolytes were studied using a diffusion-cooperative model. For the first time, we quantitatively rationalized that the limited Brownian motion of the redox centers bound to the polymers resulted in the 10^3-4-fold decline of the bimolecular and heterogeneous charge transfer rate constants, which had been unexplained for half a century. As a next-generation design, a redox-active supramolecular system with high p

A database for 240 types of lithium-ion conducting solid polymer electrolytes was newly constructed and analyzed by machine learning. Despite the complexity of the polymer composites as electrolyte...

Next‐generation, power‐efficient organic lighting systems, which ideally would be low‐cost and mass‐producible, are urgently needed because more than 20% of total electricity use goes to lighting. This study presents polymer light‐emitting electrochemical cells (PLECs) made using mass‐producible nanoimprinted corrugated substrates, which effectively improve light extraction efficiency. The corrugated substrates are fabricated using roll‐to‐roll methods, using self‐assembled block copolymers on g

Ultrathin flexible electronic devices have been attracting substantial attention for biomonitoring, display, wireless communication, and many other ubiquitous applications. In this article, organic robust redox-active polymer/carbon nanotube hybrid nanosheets with thickness of just 100 nm are reported as power sources for ultrathin devices conformable to skin. Regardless of the extreme thinness of the electrodes, a moderately large current density of 0.4 mA cm^-2 is achieved due to the

This paper evaluates the capabilities and limitations of the Generative Pre-trained Transformer 4 (GPT-4) in chemical research. Although GPT-4 exhibits remarkable proficiencies, it is evident that the quality of input data significantly affects its performance. We explore GPT-4’s potential in chemical tasks, such as foundational chemistry knowledge, cheminformatics, data analysis, problem prediction, and proposal abilities. While the language model partially outperformed traditional methods, suc

Exceptionally large output (current density over 20 mA/cm2) is achieved by a 99.9 wt % conventional LiFePO4 cathode for lithium ion batteries. Adding just 0.1 wt % redox-active fluoflavin polymer to the electrode improves the electrochemical performance dramatically. The polymer’s redox potentials of 3.3 and 3.7 V vs Li/Li+, sandwiching that of LiFePO4 (3.4 V), are critical in accelerating the charge and discharge processes by electrochemical mediation. The lower overvoltage also helps to suppre

Metal-free and totally organic based batteries were fabricated from functional polyethers. Aliphatic polyethers, in which 2,2,6,6-tetramethylpiperidin-1-oxyl and viologen were introduced with high density, were used as the cathode and anode active materials, respectively. By stacking nanosheets of the polymers and an imidazolium-substituted polyether as the electrolyte, a solid-state cell only 2 μm thick was made. The anion-type rocking-chair cell showed reversible charge/discharge even at a hig

For rapid charging of lithium-ion batteries, a series of novel electrode-active materials have been studied. However, those materials suffered from replacing conventional metal oxides, such as LiCoO2 and LiFePO4, because of the strict performance criteria for commercialization. As an alternative approach, we propose the hybridization of the conventional inorganic active materials with organic redox-active polymers which are characterized by fast electrode kinetics. A new robust organic-radical-s

Abstract In data-intensive science, machine learning plays a critical role in processing big data. However, the potential of machine learning has been limited in the field of materials science because of the difficulty in treating complex real-world information as a digital language. Here, we propose to use graph-shaped databases with a common format to describe almost any materials science experimental data digitally, including chemical structures, processes, properties, and natural languages.

Efficient screening of chemicals is essential for exploring new materials. However, the search space is astronomically large, making calculations with conventional computers infeasible. For example, an N ‐component system of organic molecules generates >10 60 N candidates. Here, a quantum‐inspired annealing machine is used to tackle the challenge of the large search space. The prototype system extracts candidate chemicals and their composites with desirable parameters, such as melting tempera

Summary A nitroxide radical‐substituted polyether, poly(TEMPO‐substituted glycidyl ether) (PTGE), was synthesized using a potassium tert ‐butoxide/18‐crown‐6 initiator. The presence of 18‐crown‐6 effected significant improvement in the reactivity of the chain end, thus allowing the polymerization to proceed at moderate temperatures to suppress the deactivation of the pendant nitroxide group. A high molecular‐weight polyether with a theoretical radical concentration was first obtained in high yie

The ionic conductivity of a liquid crystal electrolyte was switched along with redox reactions of polyviologen.

We report a deep generative model for regression tasks in materials informatics. The model is introduced as a component of a data imputer and predicts more than 20 diverse experimental properties of organic molecules. The imputer is designed to predict material properties by "imagining" the missing data in the database, enabling the use of incomplete material data. Even removing 60% of the data does not diminish the prediction accuracy in a model task. Moreover, the model excels at extrapolation

This paper evaluates the capabilities and limitations of the Generative Pre-trained Transformer 4 (GPT-4) in chemical research. Although GPT-4 exhibits remarkable proficiencies, it is evident that the quality of input data significantly affects its performance. We explore GPT-4's potential in chemical tasks, such as foundational chemistry knowledge, cheminformatics, data analysis, problem prediction, and proposal abilities. While the language model partially outperformed traditional methods, suc