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Natural biopolymers such as DNA, RNA, and proteins play a critical role in the origin of life, with their primary sequences and various folded secondary/tertiary structures enabling diverse biological functions. Generations of researchers have endeavoured to replicate these bioprocesses and produce synthetic analogues of natural biopolymers. One of the key research objectives is investigating structure-property-function relationships of synthetic biopolymer mimics. Despite continuing efforts made in this area, the effect of primary sequence on synthetic polymers is still poorly understood. The lack of powerful synthetic tools to prepare the desired polymer structures is generally recognized as the major hurdle for investigation of properties and functions. As one of the emerging synthetic techniques, single unit monomer insertion (SUMI) has been used since its advent for precise synthesis of a variety of discrete oligomers and polymers, which could be used as model compounds for investigating reaction kinetics and the relationships between primary monomer sequences and polymer properties. This body of work aims to enhance the chemical diversity of discrete oligomers synthesized by the RAFT SUMI technique and further explore the effect of monomer sequence on their resulting thermal properties. Utilizing the photoinduced-RAFT SUMI technique, the initial study demonstrated that the implementation of sequential and alternating photoinduced-RAFT SUMI is applicable to different families of vinyl monomers. A complete set of model trimers featuring different monomer sequences was successfully obtained, with broad chemical diversity. The synthesized trimers exhibit distinct synthesis kinetics, and accumulated kinetics data would help to provide full guidance for the synthesis of long chain discrete oligomers or even polymers. To this end, two pentamers with relatively high isolated yields were constructed as a proof of concept. After establishing the robustness of this synthetic method to manipulate monomer sequences in discrete oligomers, we subsequently investigated the correlation between primary sequences, molecular packing and glass transition temperatures (Tgs) of discrete unconjugated oligomers via experimental analysis and computational simulation. A crucial discovery was made in this work that distinct Tgs were observed among differently sequenced oligomers due to variance in rotational flexibility. Moreover, the sequence effect on Tgs was also demonstrated by changing monomer locations in discrete pentamers. These promising results indicated that primary sequence variations lead to notable changes in thermal behaviour, potentially allowing the understanding and design of materials for various applications, such as plasticizers for high Tg polymer materials.

We are able to understand numbers in various formats. For instance, we can read Arabic numerals (defined as the visual code in the current study) from a book and talk about these numbers (the verbal code). We can use our hands to express a number (the manual code). We can also compare the number of jellybeans in different jars without actually counting them (the semantics). How does the human brain represent these different number codes? Existing findings show that the number codes might be locally represented by unique brain regions. However, different number codes might be tightly connected to each other, e.g., one might be internally saying the number word when seeing an Arabic numeral and this potential integration between number codes has not been thoroughly explored in previous studies. Here, we used a number code localizer task to first examine the neural representation of the four number codes separately. Specifically, adults and third to fourth graders were asked to complete a number comparison task (the semantic task) and a phonological comparison (the verbal task) task in an MRI scanner. The stimuli were either Arabic numerals (the visual code) or hand images (the manual code) displaying different numbers. The contrast between different number codes yielded multiple unique brain regions supporting the neural representation of each number code. We then tested the context-dependent connectivity among all the brain regions yielded by the contrasting analyses and found that some brain regions were involved in representing more than one number code. The results stand against the localist view and support the notion that the neural representation of each number code is distributed among a network consisting of brain regions unique to each number code as well as brain regions common to several number codes.