Unsupervised vector-based classification of single-molecule charge transport data

The stochastic nature of single-molecule charge transport measurements requires collection of large data sets to capture the full complexity of a molecular system. Data analysis is then guided by certain expectations, for example, a plateau feature in the tunnelling current distance trace, and the molecular conductance extracted from suitable histogram analysis. However, differences in molecular conformation or electrode contact geometry, the number of molecules in the junction or dynamic effects may lead to very different molecular signatures. Since their manifestation is a priori unknown, an unsupervised classification algorithm, making no prior assumptions regarding the data is clearly desirable. Here we present such an approach based on multivariate pattern analysis and apply it to simulated and experimental single-molecule charge transport data. We demonstrate how different event shapes are clearly separated using this algorithm and how statistics about different event classes can be extracted, when conventional methods of analysis fail

New insights into single-molecule junctions using a robust, unsupervised approach to data collection and analysis

We have applied a new, robust and unsupervised approach to data collection, sorting and analysis that provides fresh insights into the nature of single-molecule junctions. Automation of tunneling current-distance (<i>I</i>(<i>s</i>)) spectroscopy facilitates the collection of very large data sets (up to 100 000 traces for a single experiment), enabling comprehensive statistical interrogations with respect to underlying tunneling characteristics, noise and junction formation probability (JFP). We frequently observe unusual low-to-high through-molecule conductance features with increasing electrode separation, in addition to numerous other “plateau” shapes, which may be related to changes in interfacial or molecular bridge structure. Furthermore, for the first time we use the JFP to characterize the homogeneity of functionalized surfaces at the nanoscale

Darwinism, organizational evolution and survival: key challenges for future research

How do social organizations evolve? How do they adapt to environmental pressures? What resources and capabilities determine their survival within dynamic competition? Charles Darwin’s seminal work The Origin of Species (1859) has provided a significant impact on the development of the management and organization theory literatures on organizational evolution. This article introduces the JMG Special Issue focused on Darwinism, organizational evolution and survival. We discuss key themes in the organizational evolution research that have emerged in recent years. These include the increasing adoption of the co-evolutionary approach, with a particular focus on the definition of appropriate units of analysis, such as routines, and related challenges associated with exploring the relationship between co-evolution, re-use of knowledge, adaptation, and exaptation processes. We then introduce the three articles that we have finally accepted in this Special Issue after an extensive, multi-round, triple blind-review process. We briefly outline how each of these articles contributes to understanding among scholars, practitioners and policy makers of the continuous evolutionary processes within and among social organizations and systems

MATLAB script for data sorting

Supporting information associated with the publication "New insights into single-molecule junctions using a robust, unsupervised approach to data collection and analysis", J. Am. Chem. Soc., 2015, DOI: 10.1021/jacs.5b05693. This MATLAB script is a representative example of that used to objectively sort I(s) traces obtained from 1,8-ODT-coated and blank (uncoated) Au substrates. A short 'Guide to...' document is also included to introduce the user.Supporting information associated with the publication "New insights into single-molecule junctions using a robust, unsupervised approach to data collection and analysis", J. Am. Chem. Soc., 2015, DOI: 10.1021/jacs.5b05693. This MATLAB script is a representative example of that used to objectively sort I(s) traces obtained from 1,8-ODT-coated and blank (uncoated) Au substrates. A short 'Guide to...' document is also included to introduce the user.

High Vacuum Deposition of Biferrocene Thin Films on Room Temperature Substrates

Metallocenes are a promising candidate for future spintronic devices due to their versatile and tunable magnetic properties. However, single metallocenes, e.g., ferrocene, sublimate below room temperature, and therefore the implementation for future applications is challenging. Here, a method to prepare biferrocene thin films using organic molecular beam deposition (OMBD) is presented, and the effect of substrate and deposition rate on the film structure and morphology as well as its chemical and magnetic properties is investigated. On Kapton and Si substrates, biferrocene interacts only weakly with the substrate, and distinct grains scattered over the surface are observed. By incorporating a 3,4,9,10-perylenetetracarboxylic dianhydride (PTCDA) seeding layer and depositing biferrocene at high deposition rates of 1.0 Å s–1, it is possible to achieve a well-ordered densely packed film. With spintronic applications in mind, the magnetic properties of the thin films are characterized using superconducting quantum interference device (SQUID) magnetometry. Whereas initial SQUID measurements show weak ferromagnetic behavior up to room temperature due to oxidized molecule fragments, measurements of biferrocene on PTCDA capped with LiF show the diamagnetic behavior expected of biferrocene. Through the successful deposition of biferrocene thin films and the ability to control the spin state, these results demonstrate a first step toward metallocene-based spintronics