In chemical product design, the aim is to formulate a product with desired performance. Ingredients and internal product structure are two key drivers of product performance with direct impact on the mechanical, electrical, and thermal properties. Thus, there is a keen interest in elucidating the dependence of product performance on ingredients, structure, and the manufacturing process to form the structure. Design of product structure, particularly microstructure, is an intrinsically complex problem that involves different phases of different physicochemical properties, mass fraction, morphology, size distribution, and interconnectivity. Recently, computational methods have emerged that assist systematic microstructure quantification and prediction. The objective of this paper is to review these computational methods and to show how these methods as well as other developments in product design can work seamlessly in a proposed performance, ingredients, structure, and manufacturing process framework for the design of structured chemical products. It begins with the desired target properties and key ingredients. This is followed by computation for microstructure and then selection of processing steps to realize this microstructure. The framework is illustrated with the design of nanodielectric and die attach adhesive products.
. [J]. Frontiers of Chemical Science and Engineering, 2021, 15(5): 1033-1049.
Faheem Mushtaq, Xiang Zhang, Ka Y. Fung, Ka M. Ng. Computational design of structured chemical products. Front. Chem. Sci. Eng., 2021, 15(5): 1033-1049.
Local dispersion (orientation, distance between the inclusions)
Shape of inclusion
Sphere, needle, cubic, disk, rod, fiber
Size of inclusion
Micro, nano
Interfacial interaction
Interfacial layer
Crystallinity
Single crystalline, polycrystalline, amorphous
Porosity
Pore size
Tab.2
Macrostructure
Description
Examples of products
Dispersed
One phase is dispersed in a continuous phase
DAA nanodielectrics
Porous
Presence of pores within a structure
Mosquito repellent mat Sound absorption foam
Lamellar
A structure composed of thin, flat, and interchanging lamellae of different materials
Thermal barrier coating Solar cell encapsulant film
Segregated
One phase forms a continuous network in the structure
EMI shield Piezoresistive sensor
Tab.3
Microstructural features
Examples of microstructural features parameters
Graphical representation
Phase fraction
Weight fraction, volume fraction
?
Shape of inclusion
Aspect ratio, roundedness, rectangularity
Particulate
Fibrous
Size of inclusion
Equivalent diameter, particle size distribution
Microparticles
Nanoparticles
Phase distribution
Average nearest center/surface distance between inclusion (interconnectivity), orientation
Preferred orientation
Interconnected
Interfacial interaction
Thickness of interfacial layer
?
Tab.4
Fig.5
Models
Expressions a)
Parallel model
Series model
Effective medium theory (EMT) model
Maxwell model
Hamilton model
Reciprocity model
Tab.5
Fig.6
Fig.7
Product structure
Processing routes
Property and resultant structure a)
Macro-structure
Microstructural features
Key processing techniques
Equipment design
Operating conditions
Segregated
Volume fraction, size and shape of inclusion, interconnectivity
Mechanical grinding, Hot pressing
Dimensions of mechanical grinder, and its blade design
Time and speed of grinding, time, temperature and pressure for hot pressing
Thermal conductivity= 0.37 W?m–1?K–1
Melt mixing Hot pressing
Dimensions of the mixer, and its mixing blade design
Time, temperature and mechanical power of mixing time, temperature and pressure for pressing
Thermal conductivity= 0.30 W?m–1?K–1
Tab.6
Fig.8
Processing techniques
Equipment
Functions
Process parameters
Sonication
Ultrasonicator
Disperse nanoparticles, surface modification of nanoparticles to prevent agglomeration
Sonic power, time
Mixing
Shear mixer
Disperse the silica nanoparticles in epoxy, control particle size
Mechanical speed, time
Drying
Vacuum oven
Remove solvent and coupling agent
Temperature, time
Mixing
Shear mixer
Mix the curing agent with the polymer mixture
Mechanical speed, time
Degassing
Vacuum desiccator
Remove air bubbles and moisture
Time
Casting and curing
Pre-defined mold oven
Form the solid composite
Temperature, time
Tab.7
Fig.9
Fig.10
Processing techniques
Equipment
Functions
Process parameters
Mixing
Mechanical stirrer
Mix the particles with epoxy
Mechanical speed, time
Homogenization
Homogenizer
Disperse fillers in the polymer matrix
Mechanical speed, time
Sonication
Ultrasonicator
Disperse fillers in the polymer matrix
Sonic power, time
Drying
Oven
Remove solvent
Temperature, time
Mixing
Mixer
Mix the curing agent with the polymer mixture
Mechanical speed, time
Molding and curing
Pre-defined mold oven
Form the solid composite
Temperature, time
Tab.8
1
E L Cussler, G D Moggridge. Chemical Product Design. 1st ed. Cambridge: Cambridge University Press, 2001, 1–416
2
R Gani. Chemical product design: challenges and opportunities. Computers & Chemical Engineering, 2004, 28: 2441–2457
3
R Costa, G D Moggridge, P M Saraiva. Chemical product engineering. An emerging paradigm within chemical engineering. AIChE Journal, 2006, 52: 1976–1986
4
M Hill. Chemical product engineering—the third paradigm. Computers & Chemical Engineering, 2009, 33: 947–953
5
B V Smith, M G Ierapepritou. Integrative chemical product design strategies: reflecting industry trends and challenges. Computers & Chemical Engineering, 2010, 34: 857–865
6
K M Ng, R Gani. Chemical product design: advances in and proposed directions for research and teaching. Computers & Chemical Engineering, 2019, 126: 147–156
7
G B Olson. Designing a new material world. Science, 2000, 288: 993–998
8
C Wibowo, K M Ng. Product-oriented process synthesis and development: creams and pastes. AIChE Journal, 2001, 47: 2746–2767
9
C Wibowo, K M Ng. Product-centered processing: manufacture of chemical-based consumer products. AIChE Journal, 2002, 48: 1212–1230
10
K M Ng, R Gani, K D Johansen. Chemical Product Design: Towards a Perspective Through Case Studies. 1st ed. Amsterdam: Elsevier, 2007, 165–490
11
W D Seider, S Widagdo, J D Seader, D R Lewin. Perspectives on chemical product and process design. Computers & Chemical Engineering, 2009, 33: 930–935
12
B V Smith, M G Ierapepritou. Framework for consumer-integrated optimal product design. Industrial & Engineering Chemistry Research, 2009, 48: 8566–8574
13
R Gani, K M Ng. Product design—molecules, devices, functional products, and formulated products. Computers & Chemical Engineering, 2015, 81: 70–79
14
F P Bernardo, P M Saraiva. A Conceptual model for chemical product design. AIChE Journal, 2015, 61: 802–815
15
M Mattei, G M Kontogeorgis, R Gani. A comprehensive framework for surfactant selection and design for emulsion based chemical product design. Fluid Phase Equilibria, 2014, 362: 288–299
16
Y S Cheng, K W Lam, K M Ng, R K M Ko, C Wibowo. An integrative approach to product development—a skin-care cream. Computers & Chemical Engineering, 2009, 33: 1097–1113
17
M J Bagajewicz. On the role of microeconomics, planning, and finances in product design. AIChE Journal, 2007, 53: 3155–3170
18
K Y Fung, K M Ng, L Zhang, R Gani. A grand model for chemical product design. Computers & Chemical Engineering, 2016, 91: 15–27
19
L Zhang, K Y Fung, X Zhang, H K Fung, K M Ng. An integrated framework for designing formulated products. Computers & Chemical Engineering, 2017, 107: 61–76
20
Y C Chan, K Y Fung, K M Ng. Product design: a pricing framework accounting for product quality and consumer awareness. AIChE Journal, 2018, 64: 2462–2471
21
X Zhang, L Zhang, K Y Fung, G P Rangaiah, K M Ng. Product design: impact of government policy and consumer preference on company profit and corporate social responsibility. Computers & Chemical Engineering, 2018, 118: 118–131
22
X Zhang, L Zhang, K Y Fung, K M Ng. Product design: incorporating make-or-buy analysis and supplier selection. Chemical Engineering Science, 2019, 202: 357–372
23
S Torquato. Random heterogeneous materials: microstructure and macroscopic properties. Interdisciplinary Applied Mathematics, 2002, 16: 1–656
24
D T Fullwood, S R Niezgoda, B L Adams, S R Kalidindi. Microstructure sensitive design for performance optimization. Progress in Materials Science, 2010, 55: 477–562
25
D B Brough, D Wheeler, J A Warren, S R Kalidindi. Microstructure-based knowledge systems for capturing process-structure evolution linkages. Current Opinion in Solid State and Materials Science, 2017, 21: 129–140
26
A Cecen, H Dai, Y C Yabansu, S R Kalidindi, L Song. Material structure-property linkages using three-dimensional convolutional neural networks. Acta Materialia, 2018, 146: 76–84
27
R Bostanabad, Y Zhang, X Li, T Kearney, L C Brinson, D W Apley, W K Liu, W Chen. Computational microstructure characterization and reconstruction: review of the state-of-the-art techniques. Progress in Materials Science, 2018, 95: 1–41
28
F Mushtaq. Computation-based microstructure design of structured chemical products. Dissertation for the Doctoral Degree. Hong Kong: The Hong Kong University of Science and Technology, 2020, 50–90
29
D Lu, C Liu, X Lang, B Wang, Z Li, W M P Lee, S W R Lee. Enhancement of thermal conductivity of die attach adhesive (DAAs) using nanomaterials for high brightness light emitting diode (HBLED). In: 2011 IEEE 61st Electronic Components and Technology Conference (ECTC), Lake Buena Vista, FL: IEEE, 2011, 667–672
30
J Anandraj, M G Joshi. Fabrication, performance and applications of integrated nanodielectric properties of materials: a review. Composite Interfaces, 2018, 25: 455–489
31
V Muñoz, F Buffa, F Molinari, L G Hermida, J J García, G A Abraham. Electrospun ethylcellulose-based nanofibrous mats with insect-repellent activity. Materials Letters, 2019, 253: 289–292
32
L Cao, Q Fu, Y Si, B Ding, J Yu. Porous materials for sound absorption. Composites Communications, 2018, 10: 25–35
33
G R Li, L S Wang, G J Yang. Achieving self-enhanced thermal barrier performance through a novel hybrid-layered coating design. Materials & Design, 2019, 167: 107647
34
B Lee, J Z Liu, B Sun, C Y Shen, G C Dai. Thermally conductive and electrically insulating EVA composite encapsulants for solar photovoltaic (PV) cell. Express Polymer Letters, 2008, 2: 357–363
35
C H Cui, D X Yan, H Pang, X Xu, L C Jia, Z M Li. Formation of a segregated electrically conductive network structure in a low-melt-viscosity polymer for highly efficient electromagnetic interference shielding. ACS Sustainable Chemistry & Engineering, 2016, 4: 4137–4145
36
M Wang, K Zhang, X X Dai, Y Li, J Guo, H Liu, G H Li, Y J Tan, J B Zeng, Z Guo. Enhanced electrical conductivity and piezoresistive sensing in multi-wall carbon nanotubes/polydimethylsiloxane nanocomposites via the construction of a self-segregated structure. Nanoscale, 2017, 9: 11017
37
E Conte, R Gani, K M Ng. Design of formulated products: a systematic methodology. AIChE Journal, 2011, 57: 2431–2449
38
E Conte, R Gani, Y S Cheng, K M Ng. Design of formulated products: experimental component. AIChE Journal, 2012, 58: 173–189
39
X Zhang, T Zhou, L Zhang, K Y Fung, K M Ng. Food product design: a hybrid machine learning and mechanistic modeling approach. Industrial & Engineering Chemistry Research, 2019, 58: 16743–16752
40
A Király, F Ronkay. Effect of filler dispersion on the electrical conductivity and mechanical properties of carbon/polypropylene composites. Polymer Composites, 2013, 34: 1195–1203
41
S Fu, Z Sun, P Huang, Y Li, N Hu. Some basic aspects of polymer nanocomposites: a critical review. Nano Materials Science, 2019, 1: 2–30
42
X Y Yang, L H Chen, Y Li, J R Rooke, C Sanchezc, B L Su. Hierarchically porous materials: synthesis strategies and structure design. Chemical Society Reviews, 2017, 46: 481–558
43
A G Slater, A I Cooper. Function-led design of new porous materials. Science, 2015, 348: aaa8075
44
Q Liu, F Ye, Y Gao, S Liu, H Yang, Z Zhou. Fabrication of a new SiC/2024Al co-continuous composite with lamellar microstructure and high mechanical properties. Journal of Alloys and Compounds, 2014, 585: 146–153
45
H Pang, L Xu, D X Yan, Z M Li. Conductive polymer composites with segregated structures. Progress in Polymer Science, 2014, 39: 1908–1933
46
H Pang, C Chen, Y Bao, J Chen, X Ji, J Lei, Z M Li. Electrically conductive carbon nanotube/ultrahigh molecular weight polyethylene composites with segregated and double percolated structure. Materials Letters, 2012, 79: 96–99
47
M Wang, N Pan. Predictions of effective physical properties of complex multiphase materials. Materials Science and Engineering, 2008, 63: 1–30
48
C W Nan, R Birringer, D R Clarke, H Gleiter. Effective thermal conductivity of particulate composites with interfacial thermal resistance. Journal of Applied Physics, 1997, 81: 6692–6699
49
C W Nan, Z Shi, Y Lin. A simple model for thermal conductivity of carbon nanotube-based composites. Chemical Physics Letters, 2003, 37: 666–669
50
K J Singh, R Singh, D R Chaudhary. Heat conduction and a porosity correction term for spherical and cubic particles in a simple cubic packing. Journal of Physics. D, Applied Physics, 1998, 31: 1681–1687
51
J K Carson, S J Lovatt, D J Tanner, A C Cleland. Thermal conductivity bounds for isotropic, porous materials. International Journal of Heat and Mass Transfer, 2005, 48: 2150–2158
52
J F Wang, J K Carson, M F North, D J Cleland. A new approach to modelling the effective thermal conductivity of heterogeneous materials. International Journal of Heat and Mass Transfer, 2006, 49: 3075–3083
53
Y Agari, T Uno. Estimation on thermal conductivities of filled polymers. Journal of Applied Polymer Science, 1986, 32: 5705–5712
54
X G Liang, X Ji. Thermal conductance of randomly oriented composites of thin layers. International Journal of Heat and Mass Transfer, 2000, 43: 3633–3640
55
F Mushtaq, X Zhang, K Y Fung, K M Ng. Product design: an optimization-based approach for targeting of particulate composite microstructure. Computers & Chemical Engineering, 2020, 140: doi:10.1016/j.compchemeng.2020.106975
56
Y Liu, M S Greene, W Chen, D A Dikin, W K Liu. Computational microstructure characterization and reconstruction for stochastic multiscale material design. Computer Aided Design, 2013, 45: 65–76
57
P P Mukherjee, C Y Wang. Stochastic microstructure reconstruction and direct numerical simulation of the PEFC catalyst layer. Journal of the Electrochemical Society, 2006, 153: A840–A849
58
A Sheidaei, M Baniassadi, M Banu, P Askeland, M Pahlavanpour, N Kuuttila, F Pourboghrat, L T Drzal, H Garmestani. 3-D microstructure reconstruction of polymer nano-composite using FIBSEM and statistical correlation function. Composites Science and Technology, 2013, 80: 47–54
59
W Wu, F Jiang. Microstructure reconstruction and characterization of PEMFC electrodes. International Journal of Hydrogen Energy, 2014, 39: 15894–15906
60
A Derossi, K M Gerke, M V Karsanina, B Nicolai, P Verboven, C Severini. Mimicking 3D food microstructure using limited statistical information from 2D cross-sectional image. Journal of Food Engineering, 2019, 241: 116–126
61
H Xu, Y Li, C Brinson, W Chen. A descriptor-based design methodology for developing heterogeneous microstructural materials system. Journal of Mechanical Design, 2014, 136: 051007
62
Y Zhang, H Zhao, I Hassinger, L C Brinson, L Schadler, W Chen. Microstructure reconstruction and structural equation modeling for computational design of nanodielectrics. Integrating Materials and Manufacturing Innovation, 2015, 4: 209–234
63
I Hassinger, X Li, H Zhao, H Xu, Y Huang, A Prasad, L Schadler, W Chen, L C Brinson. Toward the development of a quantitative tool for predicting dispersion of nanocomposites under non-equilibrium processing conditions. Journal of Materials Science, 2016, 51: 4238–4249
64
W Lu, R Xiao, J Yang, H Li, W Zhang. Data mining-aided materials discovery and optimization. Journal of Materiomics, 2017, 3: 191–201
65
T Zhou, Z Song, K Sundmacher. Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering, 2019, 5: 1017–1026
66
G X Gu, C T Chen, D J Richmond, M J Buehler. Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Materials Horizons, 2018, 5: 939–945
67
G H Gu, J Noh, I Kim, Y Jung. Machine learning for renewable energy materials. Journal of Materials Chemistry. A, Materials for Energy and Sustainability, 2019, 7: 17096–17117
68
G J Besseris. Taguchi-generalized regression neural network micro-screening for physical and sensory characteristics of bread. Heliyon, 2018, 3: e00551
69
M P Bendsøe, O Sigmund. Topology Optimization: Theory, Methods, and Applications. 2nd ed. New York: Springer-Verlag Berlin Heidelberg, 2004, 1–318
70
K Suzuki, N Kikuchi. A homogenization method for shape and topology optimization. Computer Methods in Applied Mechanics and Engineering, 1991, 93: 291–318
71
M P Bendsøe, O Sigmund. Material interpolation schemes in topology optimization. Archive of Applied Mechanics, 1999, 69: 635–654
72
Y M Xie, G P Steven. A simple evolutionary procedure for structural optimization. Computers & Structures, 1993, 49: 885–896
73
M Y Wang, X Wang, D Guo. A level set method for structural topology optimization. Computer Methods in Applied Mechanics and Engineering, 2003, 192: 227–246
74
W Murray, K M Ng. An algorithm for nonlinear optimization problems with binary variables. Computational Optimization and Applications, 2010, 47: 257–288
75
L Xia, P Breitkopf. Multiscale structural topology optimization with an approximate constitutive model for local material microstructure. Computer Methods in Applied Mechanics and Engineering, 2015, 286: 147–167
76
O Sigmund, K Maute. Topology optimization approaches. Structural and Multidisciplinary Optimization, 2013, 48: 1031–1055
77
J Zheng, Z Luo, H Li, C Jiang. Robust topology optimization for cellular composites with hybrid uncertainties. International Journal for Numerical Methods in Engineering, 2018, 115: 695–713
78
S Chu, L Gao, M Xia, H Li. Design of sandwich panels with truss cores using explicit topology optimization. Composite Structures, 2019, 210: 892–905
79
M Hu, J Feng, K M Ng. Thermally conductive PP/AlN composites with a 3-D segregated structure. Composites Science and Technology, 2015, 110: 26–34
80
B L Adams, A Henrie, B Henrie, M Lyon, S R Kalidindi, H Garmestani. Microstructure-sensitive design of a compliant beam. Journal of the Mechanics and Physics of Solids, 2001, 49: 1639–1663
81
D Tan, P Irwin. Polymer Based Nanodielectric Composites, Advances in Ceramics—Electric and Magnetic Ceramics, Bioceramics, Ceramics and Environment. London: IntechOpen, 2011, 115–132
82
D W Lee, B R Yoo. Advanced silica/polymer composites: materials and applications. Journal of Industrial and Engineering Chemistry, 2016, 38: 1–12
83
Z Q Yu, S L You, H Baier. Effect of organosilane coupling agents on microstructure and properties of nanosilica/epoxy composites. Polymer Composites, 2012, 33: 1516–1524
84
T Tanaka, G C Montanari, R Mülhaupt. Polymer nanocomposites as dielectrics and electrical insulation-perspectives for processing technologies, material characterization and future applications. IEEE Transactions on Dielectrics and Electrical Insulation, 2004, 11: 763–784
85
Z Cai, X Wang, B Luo, W Hong, L Wu, L Li. Dielectric response and breakdown behavior of polymer-ceramic nanocomposites: the effect of nanoparticle distribution. Composites Science and Technology, 2017, 145: 105–113
86
M A Sarami, M Moghadam, A G Gilani. Modified dielectric permittivity models for binary liquid mixture. Journal of Molecular Liquids, 2019, 277: 546–555
87
F Bellucci, D Fabiani, G C Montanari, L Testa. The processing of nanocomposites. In: Dielectric Polymer Nanocomposites. New York: Springer Science and Business Media, 2010, 31–64
88
W D Seider, D R Lewin, J D Seader, S Widegado, R Gani, K M Ng. Product and Process Design Principles: Synthesis, Analysis and Evaluation. 4th ed. New York: Wiley, 2017, 674–681
89
A S Romero, B Chen. Strategies for the preparation of polymer composites with complex alignment of the dispersed phase. Nanocomposites, 2018, 4: 137–155
