Taniike laboratory aims to implement novel materials science based on "exploration" by high-throughput experiments, "learning" by data science, and "prediction" based on high-precision molecular modeling.
Combination of different elements and materials can bear an astronomical number of possibilities. One of the purposes in materials science is to discover good combinations and a novel way (process) to produce the combinations. We perform high-throughput experimentation using highly automated and/or parallelized instruments. We maximize the throughput of the experiments and change the research style from labor-intensive to brain-intensive works.
High-throughput experimentation generates materials big data which include synthetic conditions, structural characteristics and performances of the materials. In order to implement efficient exploration of materials, it is not enough only to pick up good ones: It is necessary to clarify a structure-property relationship. Based on approaches of data science and materials informatics, we extract knowledge from the data and further accelerate materials discoveries.
Developments of computers and computational chemistry have enabled realistic simulation of complicated materials. Nonetheless, virtual materials design in computers (i.e. in-silico design) is still far to be practical. The most difficult task is how to obtain a molecular model which represents a material. We use experiments and simulations in a comprehensive fashion for establishing a high-precision molecular model towards in-silico materials design. Computational chemistry based on deep understanding of experimental materials science is aimed.
With the above three concepts, we pursue the following targets.
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:
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.
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.
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.
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.
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 variety of instruments and software are equipped in our laboratory to enhance the throughput of ideally any kinds of experiments. Our research always starts from planning and designing an optimum experimental procedure in order to maximize the throughput of the research, where existing instruments are used in combination or original instruments are developed on necessity. In the following, examples of our high-throughput research as well as our instruments and software are described.
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.
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.
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.
Precision balance (Mettler Toledo Quantos QD205DR)
Pipetting robot (Andrew+)
Multipurpose parallel reactor (modified from Büchi Synchore Q-101)
Microwave synthesizer with a robotic arm (CEM Discover SP with Explorer 72)
Parallel hot stirrer (Thermo Scientific Reacti-Therm TS-18823)
Desktop high-speed centrifuge (AS ONE AS185H)
Centrifugal evaporator (EYELA CVE-3110)
Electric furnace (FULL-TECH FT-001W)
Freeze drier (Labconco FZ2.5)
Catalyst screening instrument (developed in-house)
Photocatalyst screening instrument (developed in-house)
Operando chemiluminescence analyzer (developed in-house)
Chemiluminescence imaging instrument (developed in-house)
Parallel film casting apparatus (EYELA custom-made)
Twin-screw micro compounder (Xplore MC5)
Two-roll mixer (Imoto IMC-1104)
Hot press (AS ONE AH-2003)
Polymer properties and testing
EMS viscometer (Kyoto Electronics EMS-1000)
Polarized optical microscope (Olympus BH-2)
Tensile tester (Abecks Dat-100)
Dynamic mechanical analyzer (TA Instruments Q800-RH)
Chemiluminescence analyzer (Tohoku Electronic Industrial CLA-FS4)
Chemiluminescence imaging instrument (developed in-house)
Sun tester (Toyoseiki CPS plus)
Weatherometer (Atlas SUNTEST XXL+)
Thermal wave analyzer (Hitachi High-Tech Science ai-Phase mobile 1u/2)
LCR meter (Hioki IM3533-01)
Insulation resistance tester (Kikusui Electronics TOS5301)
Parallel pressure filtration device (developed in-house)
Atomic Force Microscope (Park NX10)
In-situ mid/far-IR spectrometer (JASCO FT/IR-6600 with Harrick Dewar DER-3-XXX)
Laser Raman spectrometer (JASCO NRS-4100)
Microplate reader (BioTek Epoch 2)
X-ray diffractometer with an autosampler (Rigaku MiniFlex600)
X-Ray fluorescence spectrometer with an autosampler (PANalytical Epsilon 3)
Thermal gravimetric analyzer (Rigaku Thermo plus EVO2)
Gas chromatograph with an autosampler (Agilent 7890A)
Automated contact angle meter (Excimer SImage AUTO 100)
Gas chromatograph (Agilent 8860 GC)
Particle size analyzer (Microtrac MT3300EX II)
Hyperspectral microscope (HinaLea 4200M)
Chemicals library (LMS Harmony R.M)
Materials Studio (BIOVIA, shared)
DX data management (Pleasanter)