Design Of Polymeric Materials Research Articles

Impact of polymer molecular weight and dispersity on the coalescence of polypropylene powders suitable for laser powder bed fusion

Understanding and controlling the fundamental processes governing the coalescence of polymers is vital to enable the design of polymeric materials for improved mechanical performance and quality of manufactured structures, including those fabricated via additive manufacturing techniques. Our group has utilized thermally induced phase separation (TIPS) to produce spherical and size controlled polypropylene (PP) powders from 12,000 (12k), 250,000 (250k), and 340,000 (340k) molecular weight (Mw) PP, including 50-50 wt% blends of 12k/250k,12k/340k, and 250k/340k, and a 33-33-33 wt% blend of 12k/250k/340k to investigate the impact of polymer Mw, and thus zero-shear viscosity, on particle coalescence and its implication in laser powder bed fusion. The particles exhibit similar size distributions with an average particle size, Dx(50), in the range of 58 μm–86 μm. The coalescence behavior of the powders, evaluated via hot-stage microscopy, show that adding 12k PP in the blend significantly alters the coalescence dynamics of the 250k and 340k PP, dramatically increasing their coalescence rate. The substantial drop in zero-shear viscosity with addition of 12k PP provides the driving force for the pronounced enhancement in coalescence. Strong agreement between experimental results and the Hopper model of coalescence is observed only if corrected for extensional flow, exemplifying the importance of extensional flow on the coalescence process. The results also indicate that the 12k PP in the blend does not surface segregate in the TIPS process, but is homogeneously distributed in the blend. More broadly, these results provide molecular-level insight into how control of powder molecular weight characteristics and viscosity can offer pathways to optimize the consolidation of particles in manufacturing processes, including laser powder bed fusion.

Solvothermal Polymerization of Diaminomaleonitrile Reveals Optimal Green Solvents for the Production of C=N-Based Polymers.

Solvothermal polymerization (STP) of diaminomaleonitrile (DAMN) was evaluated using a wide variety of solvents and in the temperature range from 80 to 170 °C. The highest yields, almost quantitative, were achieved with protic n-alcohols such as n-pentanol or n-hexanol at 130 and 150 °C, respectively. The kinetic behavior was studied by gravimetry and the DAMN consumption was monitored by UV-vis spectroscopy and HPLC. GC-MS identified byproducts of the DAMN hydrolysis and oxidation reactions, which were significantly reduced when n-pentanol or n-hexanol were used with respect to hydrothermal conditions. This led to an exploration of compositional changes and microstructural variations by FTIR and NMR spectroscopy and simultaneous thermal analysis. n-Hexanol appears to be an ideal eco-friendly solvent for the DAMN self-STP. The results presented here are not only of interest for the design of polymeric materials based on C=N structures but also show remarkable implications for prebiotic chemistry.

Predicting polymer solubility from phase diagrams to compatibility: a perspective on challenges and opportunities.

Polymer processing, purification, and self-assembly have significant roles in the design of polymeric materials. Understanding how polymers behave in solution (e.g., their solubility, chemical properties, etc.) can improve our control over material properties via their processing-structure-property relationships. For many decades the polymer science community has relied on thermodynamic and physics-based models to aid in this endeavor, but all rely on disparate data sets and use-case scenarios. Hence, there are still significant challenges to predict a priori the solubility of a polymer, whether it is for selecting sustainable solvents, obtaining thermodynamic parameters for phase separation, or navigating the coexistence curve. This perspective aims to discuss the different approaches of applying computational tools to predict polymer solubility, with a significant focus on machine learning techniques to capture the rapid progress in that space. We examine challenges and opportunities that remain for creating a comprehensive solubility toolset that can accelerate the design of a broad range of applications including films, membranes, and pharmaceuticals.

High-performance chlorinated polyvinyl chloride/polyurea nanocomposite foam with excellent solvent resistance, flame-triggered shape memory effect and its upcycling

Facing the global environmental pollution crisis related to waste plastics, the recyclable design of polymer materials and composites, especially large commodities, is the most effective and fundamental solution. Herein, an upcycling chlorinated polyvinyl chloride/polyurea (CPVC/PUA) nanocomposite foam was designed by means of the “plasticizing-foaming-reinforcing” (PFR) strategy combined with catalytic carbonization of CPVC/PUA foams. On one hand, the foam with ultra-high expansion ratio (62 times) can be facilely prepared in supercritical CO2 at lower temperature, benefited of the reactive plasticizing function of PUA monomer-polymeric methylene diphenyldiisocyanate (PMDI). On the other hand, the obtained foam is reinforced by PMDI crosslinking reaction to in situ form nano-PUA phase in the CPVC matrix and realizing robust and superior solvent resistance and flame-triggered shape memory effect. Moreover, the foam possesses remarkable ablation resistance, which is attributed to super carbonization capacity of CPVC catalyzed by PUA. This carbonization behavior endows the foam directly upcycle into functional carbon foam accompanied by the formation of HCl gas and functional aromatics. The obtained carbon foam shows attractive electromagnetic interference shielding performance, which also may be used as potential carbon source or catalyst of producing vinyl chloride monomer in the chlor-alkali industry, especially in China.

Vinyl-Addition Homopolymeization of Norbornenes with Bromoalkyl Groups.

Vinyl-addition polynorbornenes are of great interest as versatile templates for the targeted design of polymer materials with desired properties. These polymers possess rigid and saturated backbones, which provide them with high thermal and chemical stability as well as high glass transition temperatures. Vinyl-addition polymers from norbornenes with bromoalkyl groups are widely used as precursors of anion exchange membranes; however, high-molecular-weight homopolymers from such monomers are often difficult to prepare. Herein, we report the systematic study of vinyl-addition polymerization of norbornenes with various bromoalkyl groups on Pd-catalysts bearing N-heterocyclic carbene ligands ((NHC)Pd-systems). Norbornenes with different lengths of hydrocarbon linker (one, two, and four CH2 groups) between the bicyclic norbornene moiety and the bromine atom were used as model monomers, while single- and three-component (NHC)Pd-systems were applied as catalysts. In vinyl-addition polymerization, the reactivity of the investigated monomers varied substantially. The relative reactivity of these monomers was assessed in copolymerization experiments, which showed that the closer the bromine is to the norbornene double-bond, the lower the monomer's reactivity. The most reactive monomer was the norbornene derivative with the largest substituent (with the longest linker). Tuning the catalyst's nature and the conditions of polymerization, we succeeded in synthesizing high-molecular-weight homopolymers from norbornenes with bromoalkyl groups (Mn up to 1.4 × 106). The basic physico-chemical properties of the prepared polymers were studied and considered together with the results of vinyl-addition polymerization.

Interplay between entanglement and crosslinking in determining mechanical behaviors of polymer networks

ABSTRACT In polymer physics, the concept of entanglement refers to the topological constraints between long polymer chains that are closely packed together. Both theory and experimentation suggest that entanglement has a significant influence on the mechanical properties of polymers. This indicates its promise for materials design across various applications. However, understanding the relationship between entanglement and mechanical properties is complex, especially due to challenges related to length scale constraints and the difficulties of direct experimental observation. This research delves into how the polymer network structure changes when deformed. We specifically examine the relationship between entanglement, crosslinked networks, and their roles in stretching both entangled and unentangled polymer systems. For unentangled polymers, our findings underscore the pivotal role of crosslinking bond strength in determining the system’s overall strength and resistance to deformation. As for entangled polymers, entanglement plays a pivotal role in load bearing during the initial stretching stage, preserving the integrity of the polymer network. As the stretching continues and entanglement diminishes, the responsibility for bearing the load increasingly shifts to the crosslinking network, signifying a critical change in the system’s behavior. We noted a linear correlation between the increase in entanglement and the rise in tensile stress during the initial stretching stage. Conversely, the destruction of the network correlates with a decrease in tensile stress in the later stage. The findings provide vital insights into the complex dynamics between entanglement and crosslinking in the stretching processes of polymer networks, offering valuable guidance for future manipulation and design of polymer materials to achieve desired mechanical properties.

A theoretical protocol for the rational design of the bioinspired multifunctional hybrid material MIP@cercosporin.

Rational design of polymeric materials prepared with the molecular imprinting technology is gaining even more space, as it can provide the optimal conditions to direct the laboratory molecularly imprinting polymer (MIP) preparation, maximizing their efficiency while reducing costs and preparation time, when compared to try-and-error approaches. We perform a rational design of an MIP with specific cavities for cercosporin accommodation by means of computational tools. The main steps of an MIP preparation were simulated and it was found that the most appropriated functional monomer to be used in the MIP preparation for cercosporin is the acrylamide, while the most suitable crosslinking agent is found to be p-divinylbenzene. Also, the most suitable solvents to remove cercosporin from the cavity are those with low dielectric constant, such as chloroform. This kind of solvent can then be used in washing step, in the case of use the MIP for sensing destinations. On the other hand, solvents like water, which has high dielectric constants, can efficiently improve the interactions between cercosporin and the functional monomer acrylamide, being indicated when the objective is to attract or maintain the cercosporin inside the MIP cavity. Thus, a MIP@cercosporin hybrid material can be used in aqueous solutions more reliably, or even the cercosporin detection in this media can be favoured. In the selectivity analysis of the material prepared in this specific condition, the results point that this MIP can also detect elsinochrome A with high efficiency, and could be more selective for hypericin, altertoxin, hypocrelin A, and phleichrome mycotoxins. The main steps of a MIP synthesis were theoretically simulated trough density functional theory (DFT) calculations aiming to direct and optimize the synthesis and applications of the material before the bench tests. Initially, in order to choose the most suitable functional to be employed for cercosporin calculations, eight of the DFT functionals that had been previously used for cercosporin calculations in literature were tested, which were the LCWPBE, B3LYP, CAM-B3LYP, M062-X, mPW1PW91, PBE0, TPSSh, and ωb97Xd. The theoretical 1H NMR chemical shifts for cercosporin molecule were calculated and compared with experimental results to analyze the performance of the functionals. Of all these, the best results were obtained with the TPSSh functional, employing the 6-31G(d,p) basis set, and this level of theory was then used for all the following steps. All the simulations were performed by means of geometry optimizations and frequency calculations. Additionally, AIM calculations were employed for further analysis of the interactions between the chosen functional monomer and cercosporin template in step 1, which was functional monomer selection. In washing step, the calculations were done using implicit solvation model, and finally, in selectivity tests, the putative "solid" MIP was simulated by freezing the positions of the monomers after the template remotion, and then other structurally similar toxins were placed in its cavity for the geometry optimizations and frequency calculations.

Unveiling the Configurational Landscape of Carbamate: Paving the Way for Designing Functional Sequence-Defined Polymers.

Carbamate is an emerging class of a polymer backbone for constructing sequence-defined, abiotic polymers. It is expected that new functional materials can be de novo designed by controlling the primary polycarbamate sequence. While amino acids have been actively studied as building blocks for protein folding and peptide self-assembly, carbamates have not been widely investigated from this perspective. Here, we combined infrared (IR), vibrational circular dichroism (VCD), and nuclear magnetic resonance (NMR) spectroscopy with density functional theory (DFT) calculations to understand the conformation of carbamate monomer units in a nonpolar, aprotic environment (chloroform). Compared with amino acid building blocks, carbamates are more rigid, presumably due to the extended delocalization of π-electrons on the backbones. Cis configurations of the amide bond can be energetically stable in carbamates, whereas peptides often assume trans configurations at low energies. This study lays an essential foundation for future developments of carbamate-based sequence-defined polymer material design.

Closed-loop Recycling of Vinylogous Urethane Vitrimers.

Devising energy-efficient strategies for the depolymerization of plastics and the recovery of their structural components in high yield and purity is key to a circular plastics economy. Here, we report a case study in which we demonstrate that vinylogous urethane (VU) vitrimers synthesized from bis-polyethylene glycol acetoacetates (aPEG) and tris(2-aminoethyl)amine can be degraded by water at moderate temperature with almost quantitative recovery (~98%) of aPEG. The rate of depolymerization can be controlled by the temperature, amount of water, molecular weight of aPEG, and composition of the starting material. These last two parameters also allow one to tailor the mechanical properties of the final materials, and this was used to access soft, tough, and brittle vitrimers, respectively. The straightforward preparation and depolymerization of the aPEG-based VU vitrimers are interesting elements for the design of polymer materials with enhanced closed-loop recycling characteristics.

Closed‐Loop Recycling of Vinylogous Urethane Vitrimers

AbstractDevising energy‐efficient strategies for the depolymerization of plastics and the recovery of their structural components in high yield and purity is key to a circular plastics economy. Here, we report a case study in which we demonstrate that vinylogous urethane (VU) vitrimers synthesized from bis‐polyethylene glycol acetoacetates (aPEG) and tris(2‐aminoethyl)amine can be degraded by water at moderate temperature with almost quantitative recovery (≈98 %) of aPEG. The rate of depolymerization can be controlled by the temperature, amount of water, molecular weight of aPEG, and composition of the starting material. These last two parameters also allow one to tailor the mechanical properties of the final materials, and this was used to access soft, tough, and brittle vitrimers, respectively. The straightforward preparation and depolymerization of the aPEG‐based VU vitrimers are interesting elements for the design of polymer materials with enhanced closed‐loop recycling characteristics.

Synergistic effect of nano silicon and piperazine pyrophosphate/melamine polyphosphate on flame retardancy of polypropylene

To improve the flame retardant efficiency of intumescent flame retardants, some nano-scale additives as synergists were always added. In this work, nano silicon and piperazine pyrophosphate/melamine polyphosphate into polypropylene was investigated, which was obviously efficient to improve thermal stability and flame retardancy. The optimal synergistic ratio corresponded to 1 wt% SiO2, 10 wt% piperazine pyrophosphate, and 5 wt% melamine polyphosphate. The system passed the UL-94 V-1 classification and had a limiting oxygen index value of 34.5%. It exhibited a decrease on peak heat release rate and peak smoke production rate by 81% and 80%, respectively. Based on characterizations of scanning electron microscope, Fourier-transform infrared spectroscopy, and X-ray photoelectron spectroscopy, the synergistic mechanism was investigated. A reactive cross-linking network between SiO2 and piperazine pyrophosphate/melamine polyphosphate during burning was formed to enhance the structure of carbon protective layer. Nano silicon acted as a foaming nucleating agent to promote the formation of porous dense carbon layer in intumescent flame retardants during burning. The synergistic strategy of SiO2 and piperazine pyrophosphate/melamine polyphosphate provided new compounded system for the design of polymeric materials with excellent flame retardancy, great thermal stability, and low release of heat and smoke.

Machine learning quantum-chemical bond scission in thermosets under extreme deformation

Despite growing interest in polymers under extreme conditions, most atomistic molecular dynamics simulations cannot describe the bond scission events underlying failure modes in polymer networks undergoing large strains. In this work, we propose a physics-based machine learning approach that can detect and perform bond breaking with near quantum-chemical accuracy on-the-fly in atomistic simulations. Particularly, we demonstrate that by coarse-graining highly correlated neighboring bonds, the prediction accuracy can be dramatically improved. By comparing with existing quantum mechanics/molecular mechanics methods, our approach is approximately two orders of magnitude more efficient and exhibits improved sensitivity toward rare bond breaking events at low strain. The proposed bond breaking molecular dynamics scheme enables fast and accurate modeling of strain hardening and material failure in polymer networks and can accelerate the design of polymeric materials under extreme conditions.

Polyurethane coatings modified by OH-PDMS for anti-cavitation, antifouling and anticorrosion applications

Cavitation, biofouling, and seawater corrosion are important factors that reduce the service life of propellers and other overcurrent components of ship. Herein, OH-PDMS was introduced into polyurethane and a series of hydrophobic polyurethane coatings (SiPU-X) with anti-cavitation, antifouling and anticorrosion functions were synthesized by a simple addition reaction. The chemical composition and microstructure of the prepared coatings were analyzed by means of FT-IR, SEM, and EDS, and it was found that the addition of OH-PDMS enhanced the degree of microphase separation of SiPU-X. The addition of OH-PDMS resulted in a large number of island structures of OH-PDMS on the coating surface. The SiPU-15 with the optimal microphase separation had better tensile strength of 12.33 MPa, excellent hydrophobicity (with a contact angle of 105.3°), hydrolysis resistance (with a hydrolysis rate of 0.26 %), and water repellency (with a water absorption of 2.21 %). More importantly, the weight loss (7.1 mg) of SiPU-15 was much lower than that of SiPU-0 (10.0 mg) under 60 h cavitation owing to the suitable microphase structure on the surface. In addition, SiPU-15 exhibited excellent fouling release properties and could remain in the ocean for 60 days without microbial adhesion. The |Z|0.01Hz of SiPU-15 can maintain 3.33 × 107 Ω·cm2 after immersion in 3.5 wt% NaCl for 10 days. This work provides a heuristic perspective on the design of polymer materials with cavitation, fouling, and corrosion resistance performance.

Antifouling and anticorrosion function of repeatable self-healing polyurethane composite inspired by the self-healing principle of cartilage tissue

Micro-cracking caused by the impact of gravel in the ocean is an important factor affecting the longevity of the integrated antifouling and anticorrosion coating. Inspired by the animal cartilage tissue, a series of polyurethane composite coatings (MPU-FrGO) with repeatable self-healing functions were successfully prepared. MPU-FrGO are endowed with excellent antifouling and anticorrosion ability because they contain functional reduced graphene oxide (FrGO) and 2-methyl 4-isothiazoline 3-ketone (MIT). As animal cartilage tissues, two-dimensional structure of FrGO forms hydrogen bond crosslinking and chemical crosslinking with polyurethane in MPU-FrGO. The super strong spatial network endows MPU-FrGO3 (the amount of FrGO added is 1 wt%) with excellent mechanical properties and strong self-healing ability. Its stress (35.6 MPa) and strain (861%) is far more than that of PU-FrGO0 (27.2 MPa and 762%). MPU-FrGO3 showed exceptional self-healing performance, and the self-healing efficiency could reach 91% (91% of initial stress) after 15 min of NIR irradiation; the self-healing efficiency of five cycles can reach more than 83% (83% of initial stress). The |Z|0.01Hz of MPU-FrGO3 can still maintain 3.34 × 108 Ω·cm2 after immersed in 3.5 wt% NaCl for 15 days because of the excellent barrier performance of FrGO. Owing to the excellent antibacterial and anti-algal functions of FrGO and MIT, the antibacterial ability and algae inhibition coverage ability of MPU-FrGO3 were 99.5% and 97.1%, respectively. The work provides a heuristic perspective for the design of polymer materials with anticorrosion, antifouling, and self-healing.

Single-Chain Inherent Elasticity of Macromolecules: From Concept to Applications.

"The Tao begets the One. One begets all things of the world." These words of wisdom from Tao Te Ching are of great inspiration to scientists in polymer materials science and engineering: The "One" means an individual polymer chain while polymer materials consist of numerous chains. The understanding of the single-chain mechanics of polymers is crucial for the bottom-up rational design of polymer materials. With a backbone and side chains, a polymer chain is more complex than a small molecule. Moreover, an individual polymer chain is usually placed in a complicated environment (such as solvent, cosolute, and solid surface), which significantly affects the behaviors of the chain. With all these factors, it is hard to fully understand the elastic behaviors of polymers. Herein, we will first introduce the concept of the single-chain inherent elasticity of polymers, which is a fundamental property determined by the polymer backbone. Then, the applications of inherent elasticity in quantifying the effects of side chains and surrounding environment will be summarized. Finally, the challenges in related fields at present and potential research directions in the future will be discussed.

Predicting the complex stress-strain curves of polymeric solids by classification-embedded dual neural network

Predicting stress–strain curves is key to facilitate the design of polymer materials and their products with tailored mechanical response. However, due to their structural complexity, polymeric solids generally feature complex stress–strain curves, which renders it challenging to model their stress–strain behaviors. Here, using the categorized knowledge of stress–strain curves, a “Classification-Embedded Dual Neural Network (CDNN)” framework is introduced to accurately predict the mechanical evolution of polymeric solids, by taking the example of injection-molded isotactic polypropylene. Upon built, the dual model is a parallel coupling of a “curve type classifier” and a “curve feature predictor” that predict, respectively, the stress–strain curve categories and their feature points that dictate the extent of similarity between two arbitrary curves in the same category, regardless of the curve complexity. Importantly, with the aid of curve-categorized knowledge, the CDNN strategy offers an update-to-date best balance in model accuracy (20% curve error in maximum) and simplicity (300 neurons in total), which greatly enhances the model’s extrapolability and interpretability and, in turn, mitigates the demanding data requirement (27 samplings from a 4D space, that is, a material design space consisting of 4 design dimensions to tune the structure). Overall, this work establishes a simple, robust methodology in predicting polymeric solids’ stress–strain curves and is potentially generic to a variety of materials.

Block catiomers with flanking hydrolyzable tyrosinate groups enhance in vivo mRNA delivery via π–π stacking-assisted micellar assembly

ABSTRACT Messenger RNA (mRNA) therapeutics have recently demonstrated high clinical potential with the accelerated approval of SARS-CoV-2 vaccines. To fulfill the promise of unprecedented mRNA-based treatments, the development of safe and efficient carriers is still necessary to achieve effective delivery of mRNA. Herein, we prepared mRNA-loaded nanocarriers for enhanced in vivo delivery using biocompatible block copolymers having functional amino acid moieties for tunable interaction with mRNA. The block copolymers were based on flexible poly(ethylene glycol)-poly(glycerol) (PEG-PG) modified with glycine (Gly), leucine (Leu) or tyrosine (Tyr) via ester bonds to generate block catiomers. Moreover, the amino acids can be gradually detached from the block copolymers after ester bond hydrolyzation, avoiding cytotoxic effects. When mixed with mRNA, the block catiomers formed narrowly distributed polymeric micelles with high stability and enhanced delivery efficiency. Particularly, the micelles based on tyrosine-modified PEG-PG (PEG-PGTyr), which formed a polyion complex (PIC) and π–π stacking with mRNA, displayed excellent stability against polyanions and promoted mRNA integrity in serum. PEG-PGTyr-based micelles also increased the cellular uptake and the endosomal escape, promoting high protein expression both in vitro and in vivo. Furthermore, the PEG-PGTyr-based micelles significantly extended the half-life of the loaded mRNA after intravenous injection. Our results highlight the potential of PEG-PGTyr-based micelles as safe and effective carriers for mRNA, expediting the rational design of polymeric materials for enhanced mRNA delivery.

Multiscale rheology model for entangled Nylon 6 melts

AbstractA multiscale simulation method is used to calculate the rheological properties of entangled Nylon 6 melts, including the stress relaxation modulus, storage and loss moduli, and the melt viscosity. The three‐level multiscale approach includes all‐atom, coarse‐grained and slip‐spring models, each operating at different levels of resolution and encompassing a wide range of length scales and over nine orders of magnitude in time. These models are unified by matching the polymer chain structure and dynamics as well as the stress relaxation, and together predict the rheological master curves at various temperatures using time–temperature superposition. The calculated viscosity agrees reasonably with experiment. The effect of polydispersity on rheology is also studied by simulating a polydisperse melt with chain lengths follow the Schulz‐Zimm distribution. Under the same weight‐average molecular weight, the polydisperse melt shows faster stress relaxation and lower viscosity compared to the monodisperse melt. For polymers that undergo rapid degradation at elevated temperatures, such as Nylon, the proposed approach offers a useful means to investigate rheology over a wide range of conditions. Importantly, the approach is fully predictive in that calculations of rheology are generated without relying on experimental information, and it therefore offers potential for design of polymeric materials on the basis of purely molecular models.

Machine Learning-Assisted Identification of Copolymer Microstructures Based on Microscopic Images.

The microstructure of polymer materials is an important bridge between their molecular structure and macroproperties, which is of great significance to be effectively identified. With the increasing refinement of polymer material design, the microstructure of different polymer materials gradually converges, which is difficult to distinguish. In this study, the machine learning method is applied to recognize the microstructure. A highly accurate and interpretable model based on small experimental data sets has been completed by the methods of transfer learning and feature visualization, making the result of the model that can be explained from the perspective of physical chemistry. This work provides an idea for identifying microstructure and will help further promote intelligent polymer research and development.

Assessing Molecular Docking Tools to Guide the Design of Polymeric Materials Formulations: A Case Study of Canola and Soybean Protein.

After more than 40 years of biopolymer development, the current research is still based on conventional laboratory techniques, which require a large number of experiments. Therefore, finding new research methods are required to accelerate and power the future of biopolymeric development. In this study, promising biopolymer–additive ranking was described using an integrated computer-aided molecular design platform. In this perspective, a set of 21 different additives with plant canola and soy proteins were initially examined by predicting the molecular interactions scores and mode of molecule interactions within the binding site using AutoDock Vina, Molecular Operating Environment (MOE), and Molecular Mechanics/Generalized Born Surface Area (MM-GBSA). The findings of the investigated additives highlighted differences in their binding energy, binding sites, pockets, types, and distance of bonds formed that play crucial roles in protein–additive interactions. Therefore, the molecular docking approach can be used to rank the optimal additive among a set of candidates by predicting their binding affinities. Furthermore, specific molecular-level insights behind protein–additives interactions were provided to explain the ranking results. The highlighted results can provide a set of guidelines for the design of high-performance polymeric materials at the molecular level. As a result, we suggest that the implementation of molecular modeling can serve as a fast and straightforward tool in protein-based bioplastics design, where the correct ranking of additives among sets of candidates is often emphasized. Moreover, these approaches may open new ways for the discovery of new additives and serve as a starting point for more in-depth investigations into this area.