Advances in Modeling Sorption and Diffusion of Moisture in Porous Reactive Materials

Water‐vapor‐uptake experiments were performed on a silica‐filled poly(dimethylsiloxane) (PDMS) network and modeled by using two different approaches. The data was modeled by using established methods and the model parameters were used to predict moisture uptake in a sample. The predictions are reasonably good, but not outstanding; many of the shortcomings of the modeling are discussed. A high‐fidelity modeling approach is derived and used to improve the modeling of moisture uptake and diffusion. Our modeling approach captures the physics and kinetics of diffusion and adsorption/desorption, simultaneously. It predicts uptake better than the established method; more importantly, it is also able to predict outgassing. The material used for these studies is a filled‐PDMS network; physical interpretations concerning the sorption and diffusion of moisture in this network are discussed.     

Free radical entry in emulsion polymerizations

Measurements of the rate coefficients characterising the entry of free radicals into seed particles in styrene emulsion polymerizations has allowed the rate determining step for entry to be identified. This was found to be the rate of production of oligomeric species in the aqueous phase by monomer addition to the primary free radicals. Once formed the subsequent diffusion of these species to the latex particles (and their incorporation within these particles) is relatively fast, contrary to the assumptions of the previous diffusion controlled theories. The experimental results imply that the entering free radicals contain only two or three monomer units. Thermodynamic considerations show that such species should be both water soluble and surface active. Similar conclusions have been reached for other sparingly water soluble monomers, such as butyl acrylate and butyl methacrylate.     

Simulation of free radical addition reactions modified by the presence of cobalt macrocycles

Free radical addition reactions in the presence of cobaloximes and related compounds have been modelled. Several mechanisms are presented and similarities with the “persistent radical effect” noted by Daikh and Finke are discussed. Cobaloximes and salophen

1 System. name: N-acetyl-p-aminophenyl salicylate.

derivatives are widely used in organic synthesis to build carbon-carbon bonds, whereas in polymer synthesis they are used as catalytic chain transfer agents in the production of oligomers. This work shows that these reactions are closely related and also demonstrates the influence that an external radical source has on the overall reaction kinetics.     

一种新型凝胶态聚合物电解质的制备和性能

采用一种新型胶联剂新戊二醇二丙烯酸酯(noepentyl glycol diacrylate, NPGDA)和聚偏氟乙烯-六氟丙烯(poly(vinylidene fluoride-co-hexafluoropropylene), PVDF-HFP), 液态电解液组成电解质混合溶液, 然后加入引发剂并加热引发聚合反应制备了一种具有互穿聚合物网络结构的凝胶态聚合物电解质, 可以用于制备聚合物锂离子二次电池. 考察了不同PVDF-HFP/NPGDA质量比对凝胶态聚合物电解质性能的影响. 结果表明, PVDF-HFP/NPGDA质量比可以影响凝胶态聚合物电解质的结构形貌、电化学特性以及聚合物锂离子二次电池的性能. 研究发现, 当m(PVDF-HFP)/m(NPGDA)＝1:1时制备的凝胶态聚合物电解质具有较高的离子电导率和电化学稳定窗口, 室温下分别为6.99×10^-3 S•cm^-1和4.8 V(vs Li⁺/Li), 以其为电解质制备的聚合物锂离子二次电池具有较好的电化学性能.     

叠氮化氰的光解离机理

采用多参考态方法,在MRCI+Q//CAS(10,9)/6-311+G(2df)水平上对叠氮化氰(N3CN)的光解离机理进行理论研究.优化得到基态(S0)和低激发态(S1、S2、T1)势能面上的极小点、过渡态、内转换交叉点(IC-S1/S0)和隙间窜跃交叉点(ISC-S1/T1)的结构和能量,构建反应势能面.在MRCI+Q//CAS(10,9)水平上计算N3CN的垂直激发能,并和实验值进行对比.结果表明,在S0、S1、S2和T1态势能面上,N—N键断裂生成N2+NCN是主要解离途径,而C—N键断裂通道是次要通道.实验观测到220 nm处的吸收峰对应分子由S0态到S1态的激发,对应主要光解离产物为NCN[a1△g];而在275 nm处的吸收峰则对应分子被激发到T1态,然后直接生成基态产物NCN[X3Σg-].我们的理论结果与实验测量符合得很好.     

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.     

Synthesis of a macrocycle based on Linked Amino Acid Mimetics (LAAM)

We report the synthesis of a macrocycle utilizing a novel framework of standard amino acids in combination with subunits that we have named as Linked Amino Acid Mimetics (LAAMs). Macrocycles based on the LAAM concept provide both a peptide targeting region and two independently variable functional regions. In the prototype structure, the commonly known Arg-Gly-Asp (RGD) sequence was used for the targeting region. The functional regions contain a phenyl group, and the linkage was formed via a Ring-Closing Metathesis (RCM) reaction.