We demonstrate that the surface morphology and surface-wetting behavior of layer-by-layer (LbL) films can be controlled using different deposition methods. Multilayer films based upon hydrogen-bonding interactions between hydrophobically modified poly(ethylene oxide) (HM-PEO) and poly(acrylic acid) (PAA) have been prepared using the dip- and spin-assisted LbL methods. A three-dimensional surface structure in the dip-assisted multilayer films appeared above a critical number of layer pairs owing to the formation of micelles of HM-PEO in its aqueous dipping solution. In the case of spin-assisted HM-PEO/PAA multilayer films, no such surface morphology development was observed, regardless of the layer pair number, owing to the limited rearrangement and aggregation of HM-PEO micelles during spin deposition. The contrasting surface morphologies of the dip- and spin-assisted LbL films have a remarkable effect on the wetting behavior of water droplets. The water contact angle of the dip-assisted HM-PEO/PAA LbL films reaches a maximum at an intermediate layer pair number, coinciding with the critical number of layer pairs for surface morphology development, and then decreases rapidly as the surface structure is evolved and amplified. In contrast, spin-assisted HM-PEO/PAA LbL films yield a nearly constant water contact angle due to the surface chemical composition and roughness that is uniform independent of layer pair number. We also demonstrate that the multilayer samples prepared using both the dip- and spin-assisted LbL methods were easily peeled away from any type of substrate to yield free-standing films; spin-assisted LbL films appeared transparent, while dip-assisted LbL films were translucent.

Text reuse occurs in many different types of documents and for many different reasons. One form of reuse, duplicate or near-duplicate documents, has been a focus of researchers because of its importance in Web search. Local text reuse occurs when sentences, facts or passages, rather than whole documents, are reused and modified. Detecting this type of reuse can be the basis of new tools for text analysis. In this paper, we introduce a new approach to detecting local text reuse and compare it to other approaches. This comparison involves a study of the amount and type of reuse that occurs in real documents, including TREC newswire and blog collections.

A blog site consists of many individual blog postings. Current blog search services focus on retrieving postings but there is also a need to identify relevant blog sites. Blog site search is similar to resource selection in distributed information retrieval, in that the target is to find relevant collections of documents. We introduce resource selection techniques for blog site search and evaluate their performance. Further, we propose a "diversity factor" that measures the topic diversity of each blog site. Our results show that the appropriate combination of the resource selection techniques and the diversity factor can achieve significant improvements in retrieval performance compared to baselines. We also report results using these techniques on the TREC blog distillation task.

In professional search environments, such as patent search or legal search, search tasks have unique characteristics: 1) users interactively issue several queries for a topic, and 2) users are willing to examine many retrieval results, i.e., there is typically an emphasis on recall. Recent surveys have also verified that professional searchers continue to have a strong preference for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we generate Boolean queries by exploiting decision trees learned from pseudo-labeled documents and rank the suggested queries using query quality predictors. We evaluate our algorithm in simulated patent and medical search environments. Compared with a recent effective query generation system, we demonstrate that our technique is effective and general.