Catalyst Informatics

Since catalytic functions of materials are hard to predict, catalyst research has heavily relied on trials and errors with the aid of intuition and experience of individual researchers. We are promoting catalyst informatics on the basis of big data acquired by high-throughput experimentation in order to achieve the following issues:

• Rapid discoveries of promising catalysts without prior knowledge
• Extraction of catalyst design guidelines through the exploration of a vast materials space
• Elucidation of catalytic mechanism based on data-driven approaches
• Bringing breakthroughs to difficult catalytic reactions

Data-driven research premises the existence of a dataset sufficient in size, distribution, and consistency. However, such datasets are hardly available for catalysis. We have developed instruments and protocols for high-throughput experimentation, and thereby have accumulated catalyst big data necessary to realize catalyst informatics at an unprecedented scale and speed. Analysis of such data makes it possible to predict the performance of unknown catalysts and to extract catalyst design guidelines without relying on intuition or experience. Besides, the catalytic mechanism is uncovered in a data-driven manner through data-assimilated microkinetic simulation. Currently targeted reactions are chemical conversion of methane (oxidative coupling, direct methanol synthesis, reforming), exhaust gas purification, and photocatalytic water purification.
Literature: i) ACS Catal. 2021, 11, 1797; ii) ACS Catal. 2020, 10, 921; iii) https://www.alphagalileo.org/Item-Display/ItemId/204086; iv) ChemCatChem 2019, 11, 1146 ; v) Appl. Catal. A: Gen. 2020, 595, 117508; vi) https://www.youtube.com/watch?v=j_0SrPVzd3Y.

We develop instruments for high-throughput experimentation. These original instruments can produce high-quality data in a super efficient manner.

Catalyst big data acquired by high-throughput experimentation. We demonstrated the acquisition of 13000 data in 3 days, which is one order of magnitude greater than literature data for 30 years.

Catalyst design guidelines were obtained from data of randomly generated catalysts via machine learning. Materials design without prior knowledge will be soon realized.

Catalyst Structure-Performance Relationship

One advantage of solid catalysts over molecular catalysts is at the flexibility of design for multi-functionality. Integration of multiple components on a solid surface, and design of hierarchical morphology for achieving both the molecular sieving effect (selectivity) and the substrate diffusion (activity) are typical examples of strategies unique to the design of solid catalysts. In other words, the multifunctionality of catalysts is essential for simultaneously satisfying various constraints of chemical processes, but this entails increased complexity in the chemical composition and morphology of catalysts. This represents a part of attractiveness and difficulty in researching solid catalysts. If we could estimate a desired complex structure, we would be able to obtain an excellent multifunctional solid catalyst. However, the complexity itself hampers understanding of a relationship between the structure and performance, which is why trials and errors still play an important role in this field.
Our laboratory studies the structure-performance relationship (SPR) of solid catalysts to understand the origin of their multifunctionality. In particular, the SPR of the Ziegler-Natta catalyst as one of the most multifunctional solid catalysts is pursued by using a variety of approaches: high-precision molecular modeling by computational chemistry, multivariate analysis for SPR, and determination of catalyst nanostructures by machine learning and total X-ray scattering.
Literature: i) J. Catal. 2014, 311, 33; ii) J. Catal. 2012, 293, 39; iii) ACS Catal. 2019, 9, 2599; iv) J. Catal. 2020, 385, 76; v) J. Catal. 2020, 389, 525.

Advances in computers and density functional theory have realized accurate simulations of materials. On the other hand, an empirical aspect of creating a molecular model as an input forms a bottleneck of the research where a material is too complicated to deduce its structure, as in the case of solid catalysts. Our laboratory has developed a program that automatically determines catalyst nanostructures in a non-empirical fashion by combining the genetic algorithm and density functional theory. We study the significance of nanoscale heterogeneity in catalysis.

A multifunctional catalyst is structurally complicated, and its performance is determined by a complex interplay of various structural factors. We study the structure-performance relationship (SPR) of multifunctional catalysts by quantifying the structure of catalysts using various characterization methods and modeling the SPR using multivariate analysis or machine learning. Our goal is to understand how to embody mutually competitive functions (e.g. activity vs. selectivity and activity vs. stability) within a single material.

The complementary use of forward modeling via computational chemistry and backward modeling based on experimental observations is essential for understanding nanostructured functional materials, such as catalysts. Here, the total X-ray scattering technique with synchrotron radiation is employed as a means of backward modeling for quantitative determination of a catalyst nanostructure.

Development of Concept Catalysts

In addition to inorganic materials such as metals and metal oxides, which are typical for solid catalysts, our laboratory conducts research on polymers and nanomaterials such as graphene and metal organic frameworks. Such research on other materials as well as for other purposes gives us an inspiration of new concepts of catalysts. For example, a marriage of catalysis and polymer chemistry has resulted in a catalyst system that conceptually bridges two extreme ends of catalysts, i.e. molecular catalysts with well-defined but less functional features and solid catalysts with multifunctional but ill-defined features. Multiple active species grafted onto a single polymeric chain realize cooperative catalysis in a random coil as a well-defined nano-sized reactor. Use of graphene in photocatalysis and fine chemistry also has brought us excellent catalysts.
Literature: i) J. Catal. 2018, 357, 6; ii) ACS Catal. 2019, 9, 3648; iii) Carbon 2018, 133, 109; iv) Appl. Catal. A: Gen. 2018, 549, 60.

A concept originated from a fusion of catalytic and polymer chemistries. By integrating molecular catalysts into precisely synthesized polymer chains, the well-defined feature of molecular catalysts and the multifunctionality of solid catalysts can be combined. Individual polymer chains exist as random coils in the reaction solution and act as nanoreactors that exhibit cooperative catalysis.

A visible-light photocatalyst that exhibits an extremely high activity for the decomposition of organic pollutants in water. It originated from our finding in another research on the chemical exfoliation of graphite: We found that metal alkoxide could be used as an exfoliating solvent. From here, we started the research and reached a nanocomposite structure in which titanium dioxide nanosheets evenly cover graphene. Currently, the research is extended to the decomposition of marine plastics.

The structure of graphene oxide stacked via linker molecules is called graphene oxide framework (GOF). By using the uniform interlayer space, highly dispersed nanoparticles with uniform size can be synthesized. The resultant nanocomposite possesses a molecular sieving ability and can be used as a highly recyclable catalyst for various reactions.

Polymer Informatics

Just by counting the plastic products around us, it is obvious how we have been enjoying the benefits of plastics. In order to shift to a circular economy, we need to promote material or chemical recycling of plastic products, which requires a variety of technological innovations. Taniike laboratory addresses this urgent issue based on high-throughput experimentation and materials informatics.
Currently the most researched technology is related to additive formulations that improve the durability of polymers. In order to promote material recycling, it is essential to minimize the degradation that occurs during the processing and service. A variety of additives, including antioxidants, provide practical durability to polymers that are not tolerant of continuous stresses such as heat and light. Our laboratory has established technologies for ultra-efficient screening of additive formulations. In addition to the research on traditional synthetic plastics, we have participated in projects on future plastic materials such as recombinant spider silk and bio-based polymers, where our technologies are used to provide the durability to these novel materials.
Literature: i) Polym. Degrad. Stab. 2015, 121, 340; ii) ACS Appl. Polym. Mater. 2020, 8, 3319; iii) Polym. Degrad. Stab. 2018, 153, 37; iv) Polym. Chem. 2017, 8, 1049.

We developed a high-throughput chemiluminescence imaging instrument that can acquire the durability of 100 polymer samples against thermo-oxidative degradation in a single measurement. In the case of photo-induced yellowing, a further efficient protocol was developed (288 samples per measurement), enabling us to obtain data equivalent to several decades in a very short period of time.

Exploration of additive formulations based on high-throughput experimentation and a genetic algorithm. It can be seen how the genetic algorithm evolved the performance of the formulations. Durability data of 5.5 years was obtained in 50 days. We found a very effective formulation that can suppress high-temperature oxidation of a commodity plastic for more than 200 hours.

Visualization of the interaction among additives. It shows that synergistic combinations of additives are critical for the design of formulations.

Polymer Nanocomposites

Polymer nanocomposites are hybrid materials in which nanosized particles are dispersed in a polymer matrix. The general aim of the polymer nanocomposites is to add the functions of nanoparticles to a polymer while maintaining the original advantages of the polymer, such as processability and low density. If the material is properly designed, the addition of a few percent of nanoparticles could induce physical properties that are completely different from those of the original polymer. In order to achieve this, it is essential to develop various technologies, e.g. for uniformly dispersing nanoparticles with different chemical affinity from polymers, for strengthening the interfacial bonding between polymers and nanoparticles, and for simultaneously controlling the dispersion and distribution of nanoparticles.
Taniike laboratory designs original polymer nanocomposites by using our knowledge of catalysts and nanomaterials. For example, we have invented a reactor granule technology that enables uniform dispersion of various kinds of nanoparticles for a wide range of the loading without a third component like a compatibilizer. Using this or other technologies, various functional materials have been developed, such as UV-blocking transparent plastics, heat-dissipating plastics, polymer film capacitors, and highly permeable nanocomposite filtration membranes.
Literature: i) Compos. Sci. Technol. 2017, 144, 151; ii) Polymer 2017, 127, 251; iii) Compos. Sci. Technol. 2014, 102, 120; iv) Compos. Part B 2019, 162, 662; v) Polymer 2014, 55, 1012; vi) Colloids Surf. A Physicochem. Eng. Asp. 2021, 614, 126204; vii) 特願2014-265887.

A reactor granule technology is our own technology to prepare polymer nanocomposites, which is based on the impregnation of porous polymer powders with filler precursors and their chemical conversion into highly dispersed nanoparticles during melt compounding. It is a simple and versatile solution for preparing highly functional polymer nanocomposites for various applications.

A nanocomposite with highly dispersed silver nanoparticles, which exhibits glossy, antibacterial, and nonlinear optical properties.

A nanocomposite in which the dispersion and distribution of aluminum oxide nanoparticles as well as the phase separation of the polymer blend are simultaneously controlled. Uniformly distributed thermal networks result in high thermal conductivity.

HTE Catalyst Synthesis

Catalyst research typically consists of three steps: Preparation, evaluation, and analysis. High-throughput synthesis of a large number of catalysts followed by high-throughput evaluation of their performance leads to extremely fast catalyst screening. Furthermore, the obtained catalyst big data is analyzed by data science approaches to gain knowledge about catalyst design and functions. In this way, all the steps of catalyst research can be made high-throughput. There are many different processes involved in the synthesis and evaluation of catalysts. Taniike laboratory develops technologies to make high-throughput experimentation possible for each of these processes.
In catalyst preparation, parallel or automated processes such as liquid-phase synthesis of nanoparticles, impregnation, coprecipitation, hydrothermal and solvothermal synthesis have been achieved. These can be used in combination with automated weighing, liquid handling, and various work-up apparatus. For example, by parallelly impregnating various oxide supports with various nanoparticles prepared based on parallelized liquid-phase synthesis, a library of hundreds of catalysts can be obtained in a short time.

HTE Catalyst Evaluation

There are various processes (or types of reactors) for catalyst evaluation. So far, we have realized high-throughput experimentation for batch or semi-batch stirred reactions, batch photocatalytic reactions, and fixed-bed flow reactions. For example, a high-throughput catalyst evaluation system of a fixed bed flow reactor type is used to evaluate the performance of hundreds of catalysts in various reaction conditions. This leads to catalyst big data consisting of tens thousands of data points in a short period of time. The data is then analyzed using various machine learning methods to extract knowledge on catalyst design and catalysis itself.

HTE Polymer

Not only in obvious cases such as fiber-reinforced plastics and rubber-toughened plastics, polymers are mostly used by blending them with different polymers, fillers, and/or additives in order to realize various properties and functions for individual applications. The number of combinations is enormous even for a single polymer, i.e. high-throughput experimentation is an ideal technique for polymer compounding research. However, this has minimally been realized in reality. The fundamental difficulty is that the physical properties of the same formulation widely differ, depending on the compounding process.
Our laboratory has gradually improved the throughput of experiments for the polymer compounding research (albeit far from satisfactory). So far, compounding additives using parallelized solution casting, compounding fillers using a micro compounder, and measurements of various properties (durability, mechanical properties, thermal conductivity, dielectric properties, transparency, and so on) have been made feasible.