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The insertion reactivity of alkynes with the diboryl complex (Ph 3 P) 2 Pt(BCat) 2 (1, Cat ≡ {C 6 H 4 O 2 } 2-) has been investigated. Under stoichiometric conditions 1 mediates cisdiborylation of alkynes and the (PPh 3 ) 2 Pt fragment is trapped by alkyne to give the corresponding Pt-alkyne complexes. Kinetic studies under pseudo first-order conditions of alkyne indicate that the reaction is first order in 1. In the absence of added phosphine, no alkyne dependence is observed. The stoichiometric reaction is inhibited by phosphine addition, and under these conditions, a first-order dependence on alkyne concentration is observed for the disappearance of 1. The stoichiometric results exclude simple, bimolecular insertion of an alkyne into Pt-B bonds of 1, and the observed dependence on phosphine and alkyne strongly favors a mechanism where phosphine dissociation generates a threecoordinate intermediate that mediates alkyne insertion. Activation parameters for the stoichiometric alkyne insertion were derived from the temperature dependence of k obs (70-110 °C). An Eyring plot yielded the following: ∆H q ) 25.9(7) kcal/mol and ∆S q ) 4(2) eu. The rates of alkyne diborylation are also sensitive to the nature of the alkyne as 4-octyne reacts much more readily than diphenylacetylene. For para-substituted diarylacetylenes, the rate for the bis(p-trifluoromethyl) derivative is accelerated and the rate for the bis(pmethoxy) derivative is retarded relative to diphenylacetylene. The reactivity of the related diboryl complex, (PPh 3 ) 2 Pt(BPin) 2 (9, Pin ≡ {(CH 3 ) 2 CO-CO(CH 3 ) 2 } 2-), is much more complex as reductive elimination of PinB-BPin is observed before the onset of the diborylation reaction. This appears to be a general feature for this compound as elimination is promoted by various reagents (e.g., CO, PPh 3 , Me 3 Sn-SnMe 3 , and CatB-BCat). The catalytic diborylation of alkynes mediated by 1 (in the presence of added triphenylphosphine) was investigated. Kinetics experiments revealed many similarities to the stoichiometric reaction as an inverse dependence on [PPh 3 ] and first-order dependence on [alkyne] and [1] were observed. Expressions that directly relate the catalytic and stoichiometric observed rate constants were derived, and the measured values for these two systems were identical within experimental error. Thus, the data are consistent with a catalytic manifold that is identical to that observed in the stoichiometric reaction. Under catalytic conditions, the rate of alkyne diborylation exhibited no dependence on [CatB-BCat].

Polyhydroxyalkanoate-based polymersbeing ecofriendly, biosynthesizable, and economically viable and possessing a broad range of tunable propertiesare currently being actively pursued as promising alternatives for petroleum-based plastics. The vast chemical complexity accessible within this class of polymers gives rise to challenges in the rational discovery of novel polymer chemistries for specific applications. The burgeoning field of polymer informatics addresses this challenge via providing tools and strategies for accelerated property prediction and materials design via surrogate machine-learning models built on reliable past data. In this contribution, we use glass transition temperature T g as an example target property to demonstrate promise of the data-enabled route to accelerated learning of accurate structure–property mappings in PHA-based polymers. Our analysis uses a data set of experimentally measured T g values, polymer molecular weights, and a polydispersity index for PHA-based homo- and copolymers that was carefully assembled from the literature. A fingerprinting scheme that captures key properties based on topology, shape, and charge/polarity of specific chemical units or motifs forming the polymer backbone was devised to numerically represent the polymers. A validated statistical learning model is then developed to allow for a mapping of the polymer fingerprints onto the property space in a physically meaningful and reliable manner. Once developed, the model can not only rapidly predict the property of new PHA polymers but also provide uncertainties underlying the predictions. The model is further combined with an evolutionary-algorithm-based search strategy to efficiently identify multicomponent polymer compositions with a prespecified T g. While the present contribution is focused specifically on T g, the surrogate model development approach put forward here is general and can, in principle, be extended to a range of other properties.

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