Designing molecular weight distributions of arbitrary shape with selectable average molecular weight and dispersity

Controlling primary chain dispersity in network polymers: elucidating the effect of dispersity on degradation

Although dispersity has been demonstrated to be instrumental in determining many polymer properties, current synthetic strategies predominantly focus on tailoring the dispersity of linear polymers. In contrast, controlling the primary chain dispersity in network polymers is much more challenging, in part due to the complex nature of the reactions, which has limited the exploration of properties and applications. Here, a one-step method to prepare networks with precisely tuned primary chain dispersity is presented. By using an acid-switchable chain transfer agent and a degradable crosslinker in PET-RAFT polymerization, the in situ crosslinking of the propagating polymer chains was achieved in a quantitative manner. The incorporation of a degradable crosslinker, not only enables the accurate quantification of the various primary chain dispersities, post-synthesis, but also allows the investigation and comparison of their respective degradation profiles. Notably, the highest dispersity networks resulted in a 40% increase in degradation time when compared to their lower dispersity analogues, demonstrating that primary chain dispersity has a substantial impact on the network degradation rate. Our experimental findings were further supported by simulations, which emphasized the importance of higher molecular weight polymer chains, found within the high dispersity materials, in extending the lifetime of the network. This methodology presents a new and promising avenue to precisely tune primary chain dispersity within networks and demonstrates that polymer dispersity is an important parameter to consider when designing degradable materials.

Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization

The skew and shape of the molecular weight distribution (MWD) of polymers have a significant impact on polymer physical properties. Standard summary metrics statistically derived from the MWD only provide an incomplete picture of the polymer MWD. Machine learning (ML) methods coupled with high-throughput experimentation (HTE) could potentially allow for the prediction of the entire polymer MWD without information loss. In our work, we demonstrate a computer-controlled HTE platform that is able to run up to 8 unique variable conditions in parallel for the free radical polymerization of styrene. The segmented-flow HTE system was equipped with an inline Raman spectrometer and offline size exclusion chromatography (SEC) to obtain time-dependent conversion and MWD, respectively. Using ML forward models, we first predict monomer conversion, intrinsically learning varying polymerization kinetics that change for each experimental condition. In addition, we predict entire MWDs including the skew and shape as well as SHAP analysis to interpret the dependence on reagent concentrations and reaction time. We then used a transfer learning approach to use the data from our high-throughput flow reactor to predict batch polymerization MWDs with only three additional data points. Overall, we demonstrate that the combination of HTE and ML provides a high level of predictive accuracy in determining polymerization outcomes. Transfer learning can allow exploration outside existing parameter spaces efficiently, providing polymer chemists with the ability to target the synthesis of polymers with desired properties.

Molecular Weight Distribution Control for Polymerization Processes Based on the Moment-Generating Function

The molecular weight distribution is an important factor that affects the properties of polymers. A control algorithm based on the moment-generating function was proposed to regulate the molecular weight distribution for polymerization processes in this work. The B-spline model was used to approximate the molecular weight distribution, and the weight state space equation of the system was identified by the subspace state space system identification method based on the paired date of B-spline weights and control inputs. Then, a new performance criterion mainly consisting of the moment-generating function was constructed to obtain the optimal control input. The effectiveness of the proposed control method was tested in a styrene polymerization process. The molecular weight distribution of the styrene polymers can be approximated by the B-spline model effectively, and it can also be regulated towards the desired one under the proposed control method.

The effects of molecular weight dispersity on block copolymer self-assembly

The influence of dispersity in the molecular weight distributions in the core forming block for block copolymer (BCP) self-assembly is analyzed via an automated flow synthesis approach. Polystyrenes with increasing...

Controlling dispersity in aqueous atom transfer radical polymerization: rapid and quantitative synthesis of one-pot block copolymers

The dispersity (Đ) of a polymer is a key parameter in material design, and variations in Đ can have a strong influence on fundamental polymer properties. Despite its importance, current polymerization strategies to control Đ operate exclusively in organic media and are limited by slow polymerization rates, moderate conversions, significant loss of initiator efficiency and lack of dispersity control in block copolymers. Here, we demonstrate a rapid and quantitative method to tailor Đ of both homo and block copolymers in aqueous atom transfer radical polymerization. By using excess ligand to regulate the dissociation of bromide ions from the copper deactivator complexes, a wide range of monomodal molecular weight distributions (1.08 < Đ < 1.60) can be obtained within 10 min while achieving very high monomer conversions (∼99%). Despite the high conversions and the broad molecular weight distributions, very high end-group fidelity is maintained as exemplified by the ability to synthesize in situ diblock copolymers with absolute control over the dispersity of either block (e.g. low Đ → high Đ, high Đ → high Đ, high Đ → low Đ). The potential of our approach is further highlighted by the synthesis of complex pentablock and decablock copolymers without any need for purification between the iterative block formation steps. Other benefits of our methodology include the possibility to control Đ without affecting the M n, the interesting mechanistic concept that sheds light onto aqueous polymerizations and the capability to operate in the presence of air.

Tuning Ligand Concentration in Cu(0)-RDRP: A Simple Approach to Control Polymer Dispersity

Cu(0)-reversible deactivation radical polymerization (RDRP) is a versatile polymerization tool, providing rapid access to well-defined polymers while utilizing mild reaction conditions and low catalyst loadings. However, thus far, this method has not been applied to tailor dispersity, a key parameter that determines the physical properties and applications of polymeric materials. Here, we report a simple to perform method, whereby Cu(0)-RDRP can systematically control polymer dispersity (Đ = 1.07-1.72), while maintaining monomodal molecular weight distributions. By varying the ligand concentration, we could effectively regulate the rates of initiation and deactivation, resulting in polymers of various dispersities. Importantly, both low and high dispersity PMA possess high end-group fidelity, as evidenced by MALDI-ToF-MS, allowing for a range of block copolymers to be prepared with different dispersity configurations. The scope of our method can also be extended to include inexpensive ligands (i.e., PMDETA), which also facilitated the polymerization of lower propagation rate constant monomers (i.e., styrene) and the in situ synthesis of block copolymers. This work significantly expands the toolbox of RDRP methods for tailoring dispersity in polymeric materials.

Precise Control of Both Dispersity and Molecular Weight Distribution Shape by Polymer Blending

The breadth and the shape of molecular weight distributions can significantly influence fundamental polymer properties that are critical for various applications. However, current approaches require the extensive synthesis of multiple polymers, are limited in dispersity precision and are typically incapable of simultaneously controlling both the dispersity and the shape of molecular weight distributions. Here we report a simplified approach, whereby on mixing two polymers (one of high Đ and one of low Đ), any intermediate dispersity value can be obtained (e.g. from 1.08 to 1.84). Unrivalled precision is achieved, with dispersity values obtained to even the nearest 0.01 (e.g. 1.37→1.38→1.39→1.40→1.41→1.42→1.43→1.44→1.45), while maintaining fairly monomodal molecular weight distributions. This approach was also employed to control the shape of molecular weight distributions and to obtain diblock copolymers with high dispersity accuracy. The straightforward nature of our methodology alongside its compatibility with a wide range of polymerisation protocols (e.g. ATRP, RAFT), significantly expands the toolbox of tailored polymeric materials and makes them accessible to all researchers.

One Reagent with Two Functions: Simultaneous Living Radical Polymerization and Chain-End Substitution for Tailoring Polymer Dispersity

The molecular weight distribution of polymer, termed dispersity (Đ), is a fundamental parameter that determines polymer properties. Sodium azide (NaN₃) functions as a catalyst in organocatalyzed living radical polymerization when the reaction medium is nonpolar. In contrast, NaN₃ can act as a nucleophile when the reaction medium is polar. In this paper, we report an efficient approach to dispersity control by exploiting the dual functions of NaN₃ under the varied solvent polarity. Simultaneous polymerization and chain-end substitution allowed us to tune the Đ values of various polymethacrylates and poly(butyl acrylate). Notably, the Đ value could be tuned to a wide range approximately from 1.2 to 2.0 for polymethacrylates and to 3.8 for poly(butyl acrylate). This approach afforded polymer brushes on surfaces with tailored Đ values. An interesting finding was that the polymer brushes exhibited a unique interaction with external molecules, depending on the Đ value.

Tailoring polymer dispersity by mixing ATRP initiators

Herein we present a simple batch method to control polymer dispersity using a mixture of two ATRP initiators with different reactivities.

Achieving molecular weight distribution shape control and broad dispersities using RAFT polymerizations

Metered additions of chain transfer agents are used to control molecular weight distribution (MWD) features in reversible addition-fragmentation chain-transfer polymerizations, giving polymers with tailored MWD shapes and dispersities as high as 6.2.