Experimental Details Synthesis of samples Synthesis of the organically-terminated silicon nanocrystals: A 2 mL of 1-octene was treated with sodium sulfate, and was then collected into Schlenk flask. Next, the Schlenk flask was subjected to freeze-pump-thaw (FPT) cycle on a grease-free vacuum line for at least 30 min by the use of Dewar flasks filled with liquid nitrogen in order to remove the dissolved oxygen. Finally, the oxygen-free 1-octene was stored under argon atmosphere until before use. These procedures were performed on a grease-free glass vacuum line at room temperature and atmospheric pressure. A hydrogen-terminated wafer of silicon was placed in the quartz cell, and purged several times with Ar gas. Next, the quartz cell was filled with the oxygen-free 1-octene for subsequent laser ablation in liquid environment. In the cell, the target silicon was ablated for 30 min by Nd:YAG pulsed laser (λ: 532 nm, power

The emergence of pathogens resistant to existing antimicrobial drugs is a growing worldwide health crisis that threatens a return to the pre-antibiotic era. To decrease the overuse of antibiotics, molecular diagnostics systems are needed that can rapidly identify pathogens in a clinical sample and determine the presence of mutations that confer drug resistance at the point of care. We developed a fully integrated, miniaturized semiconductor biochip and closed-tube detection chemistry that performs multiplex nucleic acid amplification and sequence analysis. The approach had a high dynamic range of quantification of microbial load and was able to perform comprehensive mutation analysis on up to 1,000 sequences or strands simultaneously in <2 h. We detected and quantified multiple DNA and RNA respiratory viruses in clinical samples with complete concordance to a commercially available test. We also identified 54 drug-resistance-associated mutations that were present in six genes of Mycobacterium tuberculosis, all of which were confirmed by next-generation sequencing.

Although polydimethylsiloxane (PDMS) transfer during microcontact printing (microCP) has been observed in previous reports, which generally focused on only one or a few different substrates, in this work we investigate the extent of PDMS transfer onto a series of surfaces with a wide range of hydrophobicities using an uninked, unpatterned PDMS stamp. These surfaces include clean silicon, clean titanium, clean gold, "dirty" silicon, polystyrene, Teflon, surfaces modified with PEG, amino, dodecyl, and hexadecyl monolayers, and also two loose molecular materials. The PDMS transferred onto planar surfaces is, in general, easily detected by wetting and spectroscopic ellipsometry. More importantly, it is detected by time-of-flight secondary ion mass spectrometry (ToF-SIMS) because of the sensitivity of this technique to PDMS. The effect of surface free energy on PDMS transfer in microcontact printing is investigated, and the relationship between the amount of PDMS in ToF-SIMS spectra and the surface tensions of initial surfaces is revealed. We show that PDMS transfer can be applied as a probe of surface free energies using ToF-SIMS, where PDMS preferentially transfers onto more hydrophilic surface features during stamping, with little being transferred onto very hydrophobic surface features. Multivariate curve resolution (MCR) analysis of the ToF-SIMS image data further confirms and clarifies these results. Our data lend themselves to the hypothesis that it is the free energy of the surface that plays a major role in determining the degree of PDMS transfer during microCP.

This paper documents time-of-flight secondary ion mass spectrometry (ToF-SIMS) analyses of 34 different coal samples. In many cases, the inorganic Na + , Al + , Si + , and K + ions dominate the spectra, eclipsing the organic peaks. A scores plot of principal component 1 (PC1) versus principal component 2 (PC2) in a principal components analysis (PCA) effectively separates the coal spectra into a triangular pattern, where the different vertices of this pattern come from (i) spectra that have a strong inorganic signature that is dominated by Na + , (ii) spectra that have a strong inorganic signature that is dominated by Al + , Si + , and K + , and (iii) spectra that have a strong organic signature. Loadings plots of PC1 and PC2 confirm these observations. The spectra with the more prominent inorganic signatures come from samples with higher ash contents. Cluster analysis with the K-means algorithm was also applied to the data. The progressive clustering revealed in the dendrogram correlates extremely well with the clustering of the data points found in the scores plot of PC1 versus PC2 from the PCA. In addition, this clustering often correlates with properties of the coal samples, as measured by traditional analyses. Partial least-squares (PLS), which included the use of interval PLS and a genetic algorithm for variable selection, shows a good correlation between ToF-SIMS spectra and some of the properties measured by traditional means. Thus, ToF-SIMS appears to be a promising technique for the analysis of this important fuel.