Modeling and optimizing a polycaprolactone/gelatin/polydimethylsiloxane nanofiber scaffold for tissue engineering: using response surface methodology

Dehghan, Mahdieh; Mehrizi, Mohammad Khajeh; Nikukar, Habib

doi:10.1080/00405000.2020.1766317

Cited by 13 publications

(7 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By control of the phase structure, target properties can be achieved, and the selectable material window for a given application can be largely extended. Beyond the traditional polymer realm, the blending method has been applied in many other advanced fields, such as medical materials, 5,6 energy engineering, 7,8 and so on. 9 Blending of block copolymers has been shown to be a very good strategy for achieving fine-tuned properties of nanoassemblies.…”

Section: ■ Introductionmentioning

confidence: 99%

Strategy of “Block Blends” to Generate Polymeric Thermogels versus That of One-Component Block Copolymer

Cui

Ding

2020

Macromolecules

View full text Add to dashboard Cite

Polymer alloys are usually classified into polymer blends and block copolymers. Herein, we combine the two alloying strategies to endow materials with a thermoinduced sol–gel transition. Our system is composed of two amphiphilic block copolymers in water, and we suggest terming it a “block blend.” A series of “computer experiments” by dynamic Monte Carlo simulations were carried out to verify our idea of “block blends” and revealed the mechanism of the underlying physical gelation. A thermogel was achieved after blending two amphiphilic block copolymers with different lengths of hydrophobic and hydrophilic blocks and thus different extents of the hydrophilicity–hydrophobicity balances, in which one copolymer could otherwise always flow and the other could be insoluble in water prior to the blending. It is thus interesting that “block blends” can make non-thermogellable copolymers useful in thermogellable systems. We also examined “averaged” copolymers which shared the same composition with the block blends, and our comparative studies illustrated that the block blends impregnated richer physics. A thermogel has a percolated micelle network. Although the amphiphilicity of the copolymer determines the micelle formation, the spatial heterogeneity of two copolymers is crucial in further formation of the network, where semibald micelles are connected by hydrophobic channels rich in the relatively more hydrophobic copolymer. The block-blend strategy paves a way to generate an intelligent material, in particular, a biomaterial by hierarchical self-assembly.

show abstract

Section: ■ Introductionmentioning

confidence: 99%

Strategy of “Block Blends” to Generate Polymeric Thermogels versus That of One-Component Block Copolymer

Cui

Ding

2020

Macromolecules

View full text Add to dashboard Cite

show abstract

“…BBD has also been applied for the investigation of other TE fabrication techniques. A recent study by Dehghan et al demonstrated the use of a BBD to determine the mathematical relationship between the input factors and the responses to optimise the constructs fabricated using the electrospinning technique [ 58 ]. The study demonstrated the effect of varying the concentration of the three different constituents within a polycaprolactone/gelatine/polydimethylsiloxane (PCL/GEL/PDMS) composite biomaterial with respect to the strength, elongation, biodegradability and toxicity of the resultant electrospun constructs.…”

Section: Design Of Experiments (Doe)mentioning

confidence: 99%

“…The results from the RSM described the optimal polymer ratio to achieve the optimal mechanical properties, biodegradability and biocompatibility. The study also determined the relationship between the responses, e.g., it showed that the elongation under the mechanical loading and the biocompatibility demonstrated a quadratic relationship [ 58 ].…”

Section: Design Of Experiments (Doe)mentioning

confidence: 99%

The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research

et al. 2022

View full text Add to dashboard Cite

Optimisation of tissue engineering (TE) processes requires models that can identify relationships between the parameters to be optimised and predict structural and performance outcomes from both physical and chemical processes. Currently, Design of Experiments (DoE) methods are commonly used for optimisation purposes in addition to playing an important role in statistical quality control and systematic randomisation for experiment planning. DoE is only used for the analysis and optimisation of quantitative data (i.e., number-based, countable or measurable), while it lacks the suitability for imaging and high dimensional data analysis. Machine learning (ML) offers considerable potential for data analysis, providing a greater flexibility in terms of data that can be used for optimisation and predictions. Its application within the fields of biomaterials and TE has recently been explored. This review presents the different types of DoE methodologies and the appropriate methods that have been used in TE applications. Next, ML algorithms that are widely used for optimisation and predictions are introduced and their advantages and disadvantages are presented. The use of different ML algorithms for TE applications is reviewed, with a particular focus on their use in optimising 3D bioprinting processes for tissue-engineered construct fabrication. Finally, the review discusses the future perspectives and presents the possibility of integrating DoE and ML in one system that would provide opportunities for researchers to achieve greater improvements in the TE field.

show abstract

Section: Introductionmentioning

confidence: 99%

“…The ultimate goal in RSM is to optimize a response. The RSM has recently been employed to optimize the diameters of polyacrylic acid (PAA), 33 PCL 34 and poly (vinyl butyral) PVB 35,36 nanofibers, biodegradability, biocompatibility, stress and strain of PCL/gelatin/polydimethylsiloxane nanofibers, 37 diameter and arrangement of PAN nanofibers 38 and proton conductivity of polysulfone (PS) nanofibers. 39 This research aimed to fabricate conductive and electroconductive PGS/PCL/PPy nanofibers along with the optimization of the variables of electrospinning processes and chemical polymerization of PPy.…”

mentioning

confidence: 99%

Electro‐conductive nanofibrous structure based on PGS/PCL coated with PPy by in situ chemical polymerization applicable as cardiac patch: Fabrication and optimization

Fakhrali

Semnani

Salehi

et al. 2022

J of Applied Polymer Sci

View full text Add to dashboard Cite

As a special structure and due to the unique properties of submicron-scale fibers and electrical conductivity, conductive nanofibers recently attracted further attention in various fields, especially tissue engineering. This study aimed to coat poly (glycerol sebacate) (PGS) nanofiber/poly(caprolactone) (PCL) as a widely used combination in the fields of medicine with poly(pyrrole) (PPy) as a conductive polymer, subsequently studying and optimizing production process variables using response surface methodology (RSM). The samples were examined for morphological and structural, and their electrical conductivity and electroactivity were measured using a fourpoint probe system and a cyclic voltammetry, respectively. The electrospinning conditions for constructing the substrate nanofibers with the smallest diameter were optimized. The monomer concentration of 0.05M and the polymerization time of 3.3 h exploiting the RSM were also considered as the optimal conditions for obtaining coated-nanofibers with the smallest diameter and desired electrical conductivity (0.001 S/cm). The structural findings confirmed the successful synthesis of PGS and PPy. The conductive-nanofiber produced under optimal conditions revealed good mechanical properties (tensile strength = 3.12 ± 0.19 MPa and elongation at break = 80.26 ± 13.71%). The highest biocompatibility was observed for the sample synthesized with the pyrrole concentration of 0.05M, which can be a suitable candidate for application in cardiac tissue engineering.

show abstract

Modeling and optimizing a polycaprolactone/gelatin/polydimethylsiloxane nanofiber scaffold for tissue engineering: using response surface methodology

Cited by 13 publications

References 35 publications

Strategy of “Block Blends” to Generate Polymeric Thermogels versus That of One-Component Block Copolymer

Strategy of “Block Blends” to Generate Polymeric Thermogels versus That of One-Component Block Copolymer

The Role of Machine Learning and Design of Experiments in the Advancement of Biomaterial and Tissue Engineering Research

Electro‐conductive nanofibrous structure based on PGS/PCL coated with PPy by in situ chemical polymerization applicable as cardiac patch: Fabrication and optimization

Contact Info

Product

Resources

About