The ability to tune interparticle spatial properties of nanoparticle assemblies is essential for the design of sensing materials toward desired sensitivity and selectivity. This paper reports findings of an investigation of molecularly mediated thin film assemblies of metal nanoparticles with controllable interparticle spatial properties as a sensing array. The interparticle spatial properties are controlled by a combination of alpha,omega-difunctional alkyl mediators (X-(CH(2))(n)-X) such as alkyl dithiols, dicarboxylate acids, and alkanethiol shells capped on nanoparticles. Alkanethiolate-capped gold and gold-silver alloy nanoparticles (2-3 nm) were studied as model building blocks toward the thin film assemblies, whereas the variation of alkyl chain length manipulates the interparticle spacing. The thin films assembled on an interdigitated microelectrode array platform are characterized for determining their responses to the sorption of volatile organic compounds (VOCs). The correlation between the response sensitivity and the interparticle spacing properties revealed not only a clear dependence of the sensitivity on alkyl chain length but also the occurrence of a dramatic change of the sensitivity in a region of chain length for the alkyl mediator comparable with that of the capping alkyl chains. This finding reflects a balance between the interparticle chain-chain cohesive interdigitation and the nanostructure-vapor interaction which determines the relative change of the electrical conductivity of the inked nanoparticle thin film in response to vapor sorption. The results, along with statistical analysis of the sensor array data in terms of sensitivity and selectivity, have provided important insights into the detailed delineation between the interparticle spacing and the nanostructured sensing properties.

Scientific articles are retracted at increasing rates, with the highest rates among top journals. Here we show that a single retraction triggers citation losses through an author's prior body of work. Compared to closely-matched control papers, citations fall by an average of 6.9% per year for each prior publication. These chain reactions are sustained on authors' papers (a) published up to a decade earlier and (b) connected within the authors' own citation network by up to 4 degrees of separation from the retracted publication. Importantly, however, citation losses among prior work disappear when authors self-report the error. Our analyses and results span the range of scientific disciplines.

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We examine the evolution of economics research using a machine-learning-based classification of publications into fields and styles. The changing field distribution of publications would not seem to favor empirical papers. But economics' empirical shift is a within-field phenomenon; even fields that traditionally emphasize theory have gotten more empirical. Empirical work has also come to be more cited than theoretical work. The citation shift is sharpened when citations are weighted by journal importance. Regression analyses of citations per paper show empirical publications reaching citation parity with theoretical publications around 2000. Within fields and journals, however, empirical work is now cited more.