Supramolecular self-assembly on well-defined surfaces provides access to a multitude of nanoscale architectures, including clusters of distinct symmetry and size. The driving forces underlying supramolecular structures generally involve both graphoepitaxy and weak directional nonconvalent interactions. Here we show that functionalizing a benzene molecule with an ethyne group introduces attractive interactions in a 2D geometry, which would otherwise be dominated by intermolecular repulsion. Furthermore, the attractive interactions enable supramolecular self-assembly, wherein a subtle balance between very weak CH/π bonding and molecule-surface interactions produces a well-defined "magic" dimension and chirality of supramolecular clusters. The nature of the process is corroborated by extensive scanning tunneling microscopy/spectroscopy (STM/S) measurements and ab initio calculations, which emphasize the cooperative, multicenter characters of the CH/π interaction. This work points out new possibilities for chemical functionalization of π-conjugated hydrocarbon molecules that may allow for the rational design of supramolecular clusters with a desired shape and size.

A novel methodology was extended for modeling the detailed composition of petroleum heavy vacuum resid fractions. The resid molecules were organized in terms of basic structural attributes: cores, intercore linkages, and side chains. The identities of the structural attributes were determined both from the extrapolation of chemical characteristics of light petroleum and the analysis of detailed mass spectrometric measurement of heavy resid fragmentation products. A building block library was constructed containing ∼600 attributes. The molecular composition was constructed by the combination of attributes, or building blocks, into discrete molecules. The quantitative abundance of each molecule was determined by the juxtaposition of a set of structural attribute probability density functions (PDFs) constraining pure hydrocarbon and heteroatom mixtures. Quantitative structure–property relationships (QSPRs) were applied to calculate the bulk properties of both the constructed molecules and the mixture. The adjustable parameters of the PDFs were determined using an optimization loop that employed an objective function that contained a term for each of the available analytical data points. The resulting optimal molecular compositions were in good agreement with the experimental structural information.

A molecular-level kinetic model was developed for the gasification of common plastics, including polyethylene (PE), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS). Model development was divided into three steps: molecular characterization of the feed, generation of a pathway-level reaction network, and creation of the material balance differential equations (DEs). The structure of all polymers was modeled as linear with known repeat units. For PE, PVC, and PET, Flory−Stockmayer statistics were used to describe the initial polymer size distribution. PS was described using a twoparameter γ distribution. The parameters of all polymer size distributions were tuned using data from the literature. The chemistry of plastic gasification contains depolymerization, pyrolysis, and gasification reactions. The initial depolymerization of PE and PET was modeled using random-scission and Flory−Stockmayer statistics. A statistical method was created extending random scission to a generalized polymer size distribution and applied here to the breakdown of PS. The depolymerization of PVC was modeled as two steps: polyene formation followed by benzene production. Pyrolysis reactions were included on small oligomers and were broken down into two categories: cracking and formation of tar and char molecules. For gasification, incomplete combustion and steam reforming were included to break down oligomers, tar, and char molecules. Also, light gas reactions, e.g., water-gas shift, were added to the network. The final network contained 283 reactions and 85 species. After construction of the material balance DEs, kinetic parameters were tuned using literature data on each plastic. These studies involved gasification, pyrolysis, and thermogravimetric analysis (TGA) experiments each probing different aspects of depolymerization, pyrolysis, and gasification kinetics. Model results matched experimental data well.

A molecular-level kinetic model for biomass gasification was developed and tuned to experimental data from the literature. The development was divided into two categories: the composition of the feedstock and the construction of the reaction network. The composition model of biomass was divided into three submodels for cellulose, hemicellulose, and lignin. Cellulose and hemicellulose compositions were modeled as linear polymers using Flory–Stockmayer statistics to represent the polymer size distribution. The composition of lignin, a cross-linked polymer, was modeled using relative amounts of structural building blocks or attributes. When constructing the full biomass composition model, the fractions of cellulose, hemicellulose, and lignin were optimized using literature-reported ultimate analyses. The reaction network model for biomass contained pyrolysis, gasification, and light-gas reactions. For cellulose and hemicellulose, the initial depolymerization was described using Flory–Stockmayer statistics. The derived monomers from cellulose and hemicellulose were subjected to a full pyrolysis and gasification network. The pyrolysis reactions included both reactions to decrease the molecule size, such as thermal cracking, and char formation reactions, such as Diels–Alder addition. Gasification reactions included incomplete combustion and steam reforming. For lignin, reactions occurred between attributes and included both pyrolysis and gasification reactions. The light-gas reactions included water-gas shift, partial oxidation of methane, oxidation of carbon monoxide, steam reforming of methane, and dry reforming of methane. The final reaction network included 1356 reactions and 357 species. The performance of the kinetic model was examined using literature data that spanned six different biomass samples and had gas compositions as primary results. Three data sets from different biomass samples were used for parameter tuning, and parity plot results showed good agreement between the model and data (y predicted = y obs0.928 + 0.0003). The predictive ability of the model was probed using three additional data sets. Again, the parity plot showed agreement between the model and experimental results (y predicted = y obs0.989 – 0.007).