Material Database Research Articles

Integrated data mining for prediction of specific capacitance of porous carbon materials for flexible energy storage devices

Porous carbon materials have gained wide attention owing to their remarkable electrical conductivity and large surface area. Predicting the specific capacitance based on these materials is a crucial step towards designing and manufacturing flexible energy storage devices. To achieve this objective, we employed integrated data mining to construct a database comprising 18 input variables (such as precursor material, activation type, reference electrode, electrolyte, annealing temperature, annealing time, activation temperature, ramp rate of activation temperature, activation time, specific surface area of the pore, total volume of the pore, mesoporous volume, micropore volume, current density, activator material ratio, mass fraction, average pore size, nitrogen content) and one output variable (specific capacitance). We selected characteristic variables to build a porous carbon electrode material database and imported it into a layer fusion model and single-model such as XGBoost, LightGBM, and linear regression to calculate the influence ranking of characteristic variables. The layer fusion model was compared with the single-model by importing a blind dataset into the training, and the prediction correlation coefficient R2 of the layer fusion model was found to be 0.981, indicating superior performance compared to the single-model. This layer fusion model enables simulation predictions for the selection and preparation of porous carbon electrode materials, guides experimental design, optimizes experimental schemes, and targets experimental parameters by referencing the influence of characteristic variables to obtain optimal experimental results.

2D-2D Nanoheterostructure of an Exposed {001}-Facet CuO and MoS2 Based Bifunctional Catalyst Showing Excellent Surface Chemistry and Conductivity for Cathodic CO2 Reduction.

A novel CuO-MoS2 based heterostructure catalyst model system is proposed where a CuO nanosheet with exposed {001} facet with proper termination is the active surface for the catalysis and a MoS2 nanosheet is the supporting layer. Density functional theory (DFT) calculations were performed to validate the model. The MoS2 bilayer forms a stable heterostructure with {001} faceted CuO with different terminations exposing oxygen and copper atoms (active sites) on the surface. The heterostructure active sites with a low oxidation state of the copper atoms and subsurface oxygen atoms provide a suitable chemical environment for the selective production of multicarbon products from CO2 electrocatalytic reduction. Furthermore, our heterostructure model exhibits good electrical conductivity, efficient electron transport to active surface sites, and less interfacial resistance compared to similar heterostructure systems. Additionally, we propose a photoenhanced electrocatalysis mechanism due to the photoactive nature of MoS2. We suggest that the photogenerated carrier separation occurs because of the interface-induced dipole. Moreover, we utilized a machine learning model trained on a 2D DFT materials database to predict selected properties and compared them with the DFT results. Overall, our study provides insights into the structure-property relationship of a MoS2 supported 2D CuO nanosheet based bifunctional catalyst and highlights the advantages of heterostructure formation with selective morphology and properly terminated surface in tuning the catalytic performance of nanocomposite materials.

Structural and Electronic Properties of Two-Dimensional Materials: A Machine-Learning-Guided Prediction.

The growing number of studies and interest in two-dimensional (2D) materials has not yet resulted in a wide range of material applications. This is a result of difficulties in getting the properties, which are often determined through numerical experiments or through first-principles predictions, both of which require lots of time and resources. Here we provide a general machine learning (ML) model that works incredibly well as a predictor for a variety of electronic and structural properties such as band gap, fermi level, work function, total energy and area of unit cell for a wide range of 2D materials derived from the Computational 2D Materials Database (C2DB). Our predicted model for classification of samples works extraordinarily well and gives an accuracy of around 99 %. We are able to successfully decrease the number of studied features by employing a strict permutation-based feature selection method along with the sure independence screening and sparsifying operator (SISSO), which further supports the design recommendations for the identification of novel 2D materials with the desired properties.

The Fabrication and Mechanism of a Crystalline Organic Fluorescent Probe Based on Photoinduced Electron Transfer.

The response performances of the crystalline organic fluorescence probe are highly dependent on the long-range ordered arrangement of crystalline structure. Herein, a novel organic crystalline fluorescent probe with a high quantum yield was established through the rapid self-assembly of 1,2,4,5-Tetrakis (4-carboxyphenyl) benzene (H4TCPB) and DMF molecules. Each H4TCPB, which connects to four DMF molecules through hydrogen bonds, acts as the structural unit. The building units are packed by π-π, lone pair···π, and lone pair···lone pair interactions to form solid-state crystalline materials. H4TCPB·4DMF exhibits distinct blue fluorescent under UV light, while the quantum yield is as high as 89.02% and the fluorescence lifetime is 1.95 ns. The H4TCPB·4DMF nanocrystal exhibits a specific fluorescence quench sensibility to tetracycline (TC), compared with the common chemicals and ions in environmental water. Moreover, the test results can be obtained quickly and are easily visible to the naked eye. The limit of detection for TC is as low as 12 nM in an aqueous solution. Spectral analysis and density functional theory (DFT) theoretical calculations were used to explain the fluorescence quenching mechanism of H4TCPB·4DMF toward TC, which could be attributed to the photoinduced electron transfer occurring from H4TCPB·4DMF to TC. Our work enriches the database of crystalline luminescent materials and provides theoretical support for the design and mechanical studies of organic fluorescent probes.

Development of high-throughput rapid heat-treatment and characterization process

Heat treatment plays an important part in materials development, but traditional heat treatment methods can only develop one target temperature/cycle at a time, which impairs the experimental efficiency in the development of new materials. We have therefore introduced the use of high-throughput experiments, changed the traditional sequential iterative method to parallel processing, and developed an efficient multi-temperature-field heat-treatment method. Microwave radiation is used as the heating source, and the method takes advantage of the selective heating ability of microwaves to generate 100 temperature zones that can heat treat 100 individual sheet samples simultaneously. The method incorporates various rapid characterization techniques, which enables a large amount of characterization data to be obtained in a short time. The obtained data can be uploaded to a high-throughput materials database to provide data to support subsequent studies. In this study, high-carbon, high-chromium steel was subjected to parallel tempering heat treatment at 109–786 °C. The relationship between the tempering temperature and the material microstructure and properties was derived from the changes in the microstructure and properties after parallel tempering, and used to verify the feasibility of this method.

Accelerated Screening of Ternary Chalcogenides for Potential Photovoltaic Applications.

Chalcogenides, which refer to chalcogen anions, have attracted considerable attention in multiple fields of applications, such as optoelectronics, thermoelectrics, transparent contacts, and thin-film transistors. In comparison to oxide counterparts, chalcogenides have demonstrated higher mobility and p-type dopability, owing to larger orbital overlaps between metal-X covalent chemical bondings and higher-energy valence bands derived by p-orbitals. Despite the potential of chalcogenides, the number of successfully synthesized compounds remains relatively low compared to that of oxides, suggesting the presence of numerous unexplored chalcogenides with fascinating physical characteristics. In this study, we implemented a systematic high-throughput screening process combined with first-principles calculations on ternary chalcogenides using 34 crystal structure prototypes. We generated a computational material database containing over 400,000 compounds by exploiting the ion-substitution approach at different atomic sites with elements in the periodic table. The thermodynamic stabilities of the candidates were validated using the chalcogenides included in the Open Quantum Materials Database. Moreover, we trained a model based on crystal graph convolutional neural networks to predict the thermodynamic stability of novel materials. Furthermore, we theoretically evaluated the electronic structures of the stable candidates using accurate hybrid functionals. A series of in-depth characteristics, including the carrier effective masses, electronic configuration, and photovoltaic conversion efficiency, was also investigated. Our work provides useful guidance for further experimental research in the synthesis and characterization of such chalcogenides as promising candidates, as well as charting the stability and optoelectronic performance of ternary chalcogenides.

A Quantum-Chemical Bonding Database for Solid-State Materials

An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model for phononic properties, which shows an increase in prediction accuracies by 27% (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.

Microstructure dependent burst behavior of oxide dispersion–strengthened FeCrAl cladding

Oxide dispersion–strengthened (ODS) FeCrAl alloys are an advanced cladding material that combines the high-temperature steam oxidation resistance of conventional wrought FeCrAl with the irradiation resistance and high-temperature strength of nanostructured ferritic alloys. In the present study, the effect of microstructure on the performance of the ODS FeCrAl alloy system in light-water reactor–accident scenarios is evaluated using a combination of experimentation and modeling. ODS FeCrAl alloy with nominal composition Fe-12Cr-6Al-0.3Zr-0.3Y2O3 was produced into a thin-walled tube via high-precision tube rolling. A portion of the as-received (AR) tube was subjected to a full recrystallization heat treatment. Both variants were subjected to burst testing at Oak Ridge National Laboratory’s Severe Accident Test Station. The recrystallized tube exhibited burst temperature margins 200 °C higher than that of C26M, whereas the AR tube only achieved burst properties comparable to the wrought FeCrAl alloy. The burst experiments were simulated in the BISON finite element code through the development of new viscoplastic material databases for each heat treatment condition, informed by high temperature tensile tests of tube specimens and existing literature data on creep mechanisms. Finally, an ODS FeCrAl burst criterion was obtained using simulated burst experiments across the entire range of test conditions.

P16.09.B NEUROGENIC DYSPHAGIA IN MENINGEOSIS NEOPLASTICA PATIENTS

Abstract BACKGROUND Meningeosis neoplastica (MN) occurs in 5-10 % of all cancers and is characterized by diffuse seeding of tumor cells in the cerebrospinal fluid (CSF) and meninges. Common clinical symptoms suggestive of MN include headache, cranial nerve (CN) deficits, psychiatric symptoms as well as paresthesia. Neurogenic dysphagia (ND) has been suggested a rare MN symptom resulting from affection of CN IX and X. The aim of this work was to investigate frequency, severity and pattern of malfunction of ND due to MN as well as to assess its impact on the rate of pneumonia and overall survival. MATERIAL AND METHODS The database of the Institute of Neurooncology of the University Hospital Frankfurt was screened for patients with MN diagnosed between March 2009 and July 2021 who received a systematic dysphagia assessment (DA). DA comprised a standardized clinical swallowing examination as well as an additional fiber optic endoscopic evaluation of the swallowing act (FEES) in case of suspected aspiration. Severity of dysphagia and extent of oral impairment were systematically recorded. In addition to demographics, clinical diagnoses, underlying cancer and treatment, information on radiological imaging including presence of contrast-enhancing lesions was collected. RESULTS A total of 262 patients with a diagnosis of MN were detected, 69 of whom (52 % women, mean age 61 ± 14 years) were included for further analysis. Underlying cancers included lung cancer (16 cases, 23 %), breast cancer (15, 22 %), as well as lymphoma and melanoma (each 7 cases, 10%). The remaining 24 patients suffered from cancer entities (ovarian cancer, gastrointestinal tumors or cancers of unknown primary). 46 patients (66.6 %) were diagnosed with ND, 15 of these patients required a feeding tube (FOIS 1-3). Dysphagic patients had an increased risk of pneumonia (p<0.001) and a shorter survival time compared to non-dysphagic patients (p=0.003). Affection of the cranial nerves affected CN V (n=6), CN VII (n=22), CN IX (n=21), CN X (n=7) and CN XII (n=12). There was no significant correlation between the presence of an ND and the above-mentioned cranial nerve deficits. CONCLUSION The results of this study show that ND is a common symptom in patients with MN and can also occur independently of paresis of CN IX and X. In addition, the presence of ND is associated with a higher rate of pneumonia and a shorter survival time. Therefore, patients with suspected or confirmed MN should receive a systematic dysphagia assessment. In addition, the presence of MN should also be considered in the differential diagnosis of unclear neurogenic dysphagia.

Prediction model of type and band gap for photocatalytic g-GaN-based van der Waals heterojunction of density functional theory and machine learning techniques

Applying suitable two-dimensional (2D) heterojunctions to photocatalytic water cracking reaction can obtain excellent catalytic performance. However, due to many candidate materials and complex interface effects, finding suitable heterojunctions combinations has become a challenge for the above applications. Based on about 1000 pieces of material data in the computational 2D material database, we adopted a simple energy band shift hypothesis to creatively build a machine learning prediction model of g-GaN based 2D Van der Waals (vdW) heterostructures’ type with good performance, and carried out a first-principle calculation to verify the hypothesis. The results show that the band shift hypothesis is valid for the g-GaN based vdW heterojunctions combination without the participation of elements locatedin thefirsttransitional period and this classification model with the area under curve (AUC) value of 0.93. In addition, we further built a regression prediction model for the band gap value of type Ⅱ g-GaN based vdW heterojunctions in line with the band edge position of photocatalytic water-splitting reaction, with a mean absolute error (MAE) of 0.24 eV. This work establishes a machine learning screening process for g-GaN based vdW heterojunction applied in the field of photocatalysis, which greatly improves the research efficiency.

Prediction of organic molecular optical absorption energy based on deep learning using mordred descriptor features

Organic molecules have several characteristics based on optical absorption. A molecule absorbs a specific light wavelength that represents electronic energy in the material. The absorption wavelength is also related to atomic binding of molecules that interact with material during light exposure producing electronic instability in the material. Molecular absorption can be measured using several optical spectrometer configurations that contain a light source, optical path, and light detector. The measurements of molecular optical absorption have been reported in several research and have been collected into a material database. In this paper, we developed a model for predicting organic molecular optical absorption using deep learning. The model can predict molecular absorption energy based on extracted features of molecular structure using Mordred descriptor features extraction. Our model used 1625 molecules of absorption in several solvent datasets that split into 80% training and 20% testing dataset. The result show that our model has a good agreement with experimental data with correlation coefficient 0.96 and mean average error 0.172 eV.

Recycling materials database as a Green Kaizen for sustainable development in the automotive industry

The article aims to present a proposal and discuss the Green Kaizen Model, which is focused on integrated development, ensuring cost reduction and fulfilling the requirements of all the organisation’s interested parties.The authors have prepared and presented the Green Kaizen Model in the study. Its effectiveness has been confirmed in the balanced development of the DPF production process.The development of the model, based on the multiannual experience of the automotive-oriented organisation and its systemic application, has confirmed that its implementation brings measurable benefits in the range of limiting the environmental aspects.The paper should be of special attention to engineers interested in applying Green Kaizen and recycling materials databases to develop processes.The value of the presented paper is constituted by the Green Kaizen Model. The proposed model may create a complex tool for improving the costs and quality range processes and be dedicated to sustainable development.

AI-based estimator for computational discovery and synthesis of customized microwave absorbing materials

This article investigates the viability of a deep neural network (DNN) for the computational discovery and synthesis of efficient microwave-absorbing materials and structures. A DNN is trained to tackle specific objectives in a constrained environment by utilizing the conventional forward and reverse approaches. In the forward approach, the DNN predicts various topologies of the absorbers and it is found to be effective in determining the stacking sequence of microwave-absorbing materials and their associated thicknesses. Designing a microwave absorber is observed to be exceptionally cumbersome utilizing a DNN if the material database increases unexpectedly. Following that, the solution is offered by addressing the reverse approach, in which a DNN is utilized to forecast the electromagnetic (EM) properties based on user-defined specifications. It is a convincing and simple method of designing thin and wideband customized absorbers. DNN prediction is authenticated by fabricating two distinct absorbers based on the frequency-dependent EM properties. Furthermore, the synthesized model is tested and validated with the response of the EM mixing model and microwave measurements. The suggested DNN strategy can effectively fix the issues in designing thin and broadband absorbers.

On the application of high‐throughput experimentation and data‐driven approaches in metallic glasses

AbstractMaterials genome engineering (MGE) has been successfully applied in various fields, resulting in a series of novel materials with excellent performance. Significant progress has been made in high‐throughput simulation, experimentation, and data‐driven techniques, enabling the effective prediction, rapid synthesis, and characterization of many classes of materials. In this brief review, we introduce the achievements made in the field of metallic glasses (MGs) using MGE, in particular high‐throughput experimentation and data‐driven approaches. High‐throughput experiments help to efficiently synthesize and characterize many materials in a short period of time, enabling the construction of high‐quality material databases for data‐driven methods. Paired with machine learning, potential alloys of desired properties may be revealed and predicted. Along with the progress in computational power and algorithms of machine learning, the complex composition‐structure‐properties relationship is hopefully established, which in turn help efficient and precise prediction of new MGs.

Discovery of New Fast Oxygen Conductors: Bi2MO4x (M= rare earth, X= halogen) Via Unsupervised Machine Learning

Discovering new fast oxygen conductors is critical for developing many technologies, e.g., solid oxide fuel cells (SOFCs), solid oxide electrolysis cells (SOECs), proton ceramic fuel cells (PCFCs), and solid oxide air batteries, particularly for operation at lower temperatures (below 700 ˚C). By leveraging unsupervised machine learning techniques and large databases of computed materials data (e.g., Materials Project), we are able to rapidly explore and screen for new materials with high oxygen mobility. In this work, we used unsupervised machine learning analysis applied to features extracted from the structure of materials and discovered a new structural class of vacancy-mediated oxygen conductor. To do this, we applied the K-nearest neighbor (k-NN) algorithm on all the approximately 62,000 oxygen-containing compounds in the Materials Project database, using X-ray diffraction (XRD) and pair distribution function (PDF) data to represent the structural features. The neighbors of reference known for good oxygen conductors were screened with criteria relating to material stability, synthesizability, and electronic property dissimilarity (to ensure a novel material) compared to the reference material. Starting with the best-known oxygen conductor, the fluorite-type material, we examined the materials list of its nearest neighbors and identified the Bi2MO4X (M=Rare-earth element, X= halogen element) structural family as a novel group of fast oxygen conductors. In particular, Bi2LaO4Cl is a layered bismuth oxyhalide crystallized in the space group P4/mmm, adopting a “triple fluorite” layer [Bi2LaO4]+, balanced by the anion Cl- layer. The stability and mobility of oxygen vacancy in Bi2LaO4Cl were studied using Density Functional Theory (DFT) methods. 2+ oxygen vacancy is the most thermodynamically favorable defect type. Ab initio and machine learning-trained interatomic potential molecular dynamic simulations were performed to study the high temperature oxygen mobility in Bi2LaO4Cl. The simulation results show that Bi2LaO4Cl presents an extremely low migration barrier of 0.14 +/- 0.03 eV below 700 ˚C, suggesting 10 to 20 times higher ionic conductivity compared to yttria-stabilized zirconia (YSZ). Bi2LaO4Cl is an insulator with a wide band gap of 2.65 eV, indicating that Bi2LaO4Cl may be a promising electrolyte material with high oxygen ion conductivity and low electronic conductivity. This work demonstrates the power of combining unsupervised machine learning approaches with large materials databases for finding new fast oxygen conductors.

Predicting Fast Oxygen Ion Conductors Via Descriptors for Migration Barrier Based on Electronic Structure and Lattice Dynamics

Solid-state oxygen ion conductors are of paramount importance to solid oxide fuel cells and electrolytic cells. However, with the oxygen ion conductors proposed to date, these technologies require too high (close to 800 oC) temperatures to operate, leading to technological challenges and high costs. Therefore, it is of primary importance to design faster oxygen ion conductors at intermediate and low temperatures (below 400 oC) that can enable sustainable energy technologies to mitigate climate change. Descriptors for oxygen ion conduction are highly desirable to identify promising materials, which can be done by leveraging fast-growing statistical and machine learning approaches for screening of material databases [1]. Recently, computational tools combined with experiments have allowed researchers to gain atomistic insights and relate ion transport to features of the lattice dynamics [2] and electronic structure [3] of a given material. In this work, building upon the previous literature, we leverage both the lattice dynamics and electronic structure to predict the migration barrier for oxygen migration in pure and mixed electronic ionic conductors and search for promising oxygen ion conductors.Using density functional theory simulations, we model oxygen migration in perovskite, rutile, fluorite, and Ruddlesden-Popperoxides. We analyze oxygen ion transport in pure and mixed electronic ionic conductors and oxygen vacancies with and without charge. We study changes in the electronic structure during oxygen migration within the solid host lattice in different oxides. By analyzing trends of migration barrier and electronic density of states, we relate the migration barrier to the electronic structure of the transition state. Our results show that the migration barrier depends on the capability that the host lattice has of screening charge associated with the mobile ion in the transition state, which if increased would lower the localization of charge around the mobile ion and in turn the oxygen migration barrier. In pure ionic conductors – where no charge is associated with the mobile ion - the migration barrier is largely dependent on the M-O strength. Based on the mechanistic understanding derived by these initial analyses, we further relate oxygen ion conductivity to the lattice dynamics. We show that in pure ionic conductors, increasing migration barrier follows trends of increasing oxygen vibrational frequency based on phonon-electron coupling, which also relates the prefactor of Arrhenius law with the migration barrier. However, when trying to predict trends of the migration barrier across different crystal structures or mixed ionic conductors, no single descriptor can capture the complex physiochemistry behind oxygen migration. Thus, we leverage statistical learning tools to identify multivariate descriptors and analyze specific features of lattice moieties that improve oxygen ion conduction. The results of this work provide mechanistic understanding of oxygen ion migration and strategies to accelerate the designs of fast oxygen ion conductors.

Gel point investigation of liquid silicone rubber using rheological approaches

Liquid silicone rubber (LSR) is becoming increasingly popular due to its chemical, UV, and heat resistance as well as its excellent optical properties. However, there is currently a lack of research and understanding of the rheological properties of the material. During the processing of liquid silicone rubber, the material can assume different rheological states. In the present work, both the physical and the chemical gel behavior are investigated rheologically. For this purpose, measurements are carried out with a rotational rheometer and a high-pressure capillary rheometer. Depending on the temperature and pre-shear rate, times are given for the physical gel point, after which a significantly destroyed physical network is recovered. Investigations at higher temperatures, which lead to chemical crosslinking and, therefore, to a chemical gel point, resulted in a representation of the temperature-dependent chemical gel point. The presented results serve as a material database for reliable simulations.

Stability and Equilibrium Structures of Unknown Ternary Metal Oxides Explored by Machine-Learned Potentials.

Ternary metal oxides are crucial components in a wide range of applications and have been extensively cataloged in experimental materials databases. However, there still exist cation combinations with unknown stability and structures of their compounds in oxide forms. In this study, we employ extensive crystal structure prediction methods, accelerated by machine-learned potentials, to investigate these untapped chemical spaces. We examine 181 ternary metal oxide systems, encompassing most cations except for partially filled 3d or f shells, and determine their lowest-energy crystal structures with representative stoichiometry derived from prevalent oxidation states or recommender systems. Consequently, we discover 45 ternary oxide systems containing stable compounds against decomposition into binary or elemental phases, the majority of which incorporate noble metals. Comparisons with other theoretical databases highlight the strengths and limitations of informatics-based material searches. With a relatively modest computational resource requirement, we contend that heuristic-based structure searches, as demonstrated in this study, offer a promising approach for future materials discovery endeavors.

Hydration and Fractal Analysis on Low-Heat Portland Cement Pastes Using Thermodynamics-Based Methods

Low-heat Portland (LHP) cement is a kind of high-belite cement, which has the characteristic of low hydration heat. Currently, it is extensively used in the temperature control of mass concrete. Based on the thermodynamic database of OPC-based materials, the thermodynamic software GEM-Selektor (noted as GEMS) is used for simulating the hydration products of the LHP cement paste. Then, according to the GEMS thermodynamic simulation results, MATLAB is used to visualize the initial and ultimate stages of LHP cement pastes; the effects of curing temperature and water to cement (w/c) ratio on hydration products are addressed; and the porosity, fractal dimension, and tortuosity of different pastes are calculated. It is found that an appropriately high curing temperature is important for reducing porosity, especially in the early hydration stage. Hydration time also has a significant impact on the hydration of LHP cement paste; long hydration time may reduce the impact of temperature on hydration products. The w/c ratio is another important consideration regarding the hydration degree and porosity of LHP paste, and under different curing temperatures, hydration times, and w/c ratios, the porosity varies from 5.91–32.91%. The fractal dimension of this work agrees with the previous findings. From tortuosity analysis, it can be concluded that the high curing temperature may cause significant tortuosity, further affecting the effective diffusivity of LHP cement paste. For cement pastes with low w/c ratio, this high curing temperature effect is mainly reflected in the early hydration stage, for ones with high w/c ratio, it is in turn evident under long-term curing.

Intelligent co-design of material, process, and equipment for manufacturing high-quality traditional Chinese medicine preparations

In the context of Pharma 4.0, the design tools that support the pharmaceutical Quality by Design(QbD) are iterating fast toward intelligent or smart design. The conventional development methods for traditional Chinese medicine(TCM) preparations have the limitations such as over dependence on experience, low dimensions for the designed experiment parameters, poor compatibility between the process and equipment, and high trial-and-error cost during process scale-up. Therefore, this paper innovatively proposed the intelligent co-design involving material, process, and equipment for manufacturing high-quality TCM preparations, and introduced the design philosophy, targets, tools, and applications with TCM oral solid dosage(OSD) as an example. In terms of design philosophy, the pharmaceutical design tetrahedron composed of critical material attributes, critical process parameters, critical equipment attributes, and critical quality attributes was developed. The design targets were put forward based on the product performance classification system. The design tools involve a design platform that contains several modules, such a as the iTCM material database, the processing route classification system, the system modeling and simulation, and reliability-based optimization. The roles of different modules in obtaining essential and universal design knowledge of the key common manufacturing units were introduced. At last, the applications of the co-design methodology involving material, process, and equipment in the high shear wet granulation process development and the improvement of the dissolving or dispersion capability of TCM formula granules are illustrated. The research on advanced pharmaceutical design theory and methodology will help enhance the efficiency and reliability of drug development, improve the product quality, and promote the innovation of high-end TCM products across the industry.