Controlling the surface silanol density in capillary columns and planar silicon via the self-limiting, gas-phase deposition of tris(dimethylamino)methylsilane, and quantification of surface silanols after silanization by low energy ion scattering.

Surface silanols (Si-OH) play a vital role on fused silica surfaces in chromatography. Here, we used an atmospheric-pressure, gas-phase reactor to modify the inner surface of a gas chromatography, fused silica capillary column (0.53 mm ID) with a small, reactive silane (tris(dimethylamino)methylsilane, TDMAMS). The deposition of TDMAMS on planar witness samples around the capillary was confirmed with X-ray photoelectron spectroscopy (XPS), ex situ spectroscopic ellipsometry (SE), and wetting. The number of surface silanols on unmodified and TDMAMS-modified native oxide-terminated silicon were quantified by tagging with dimethylzinc (DMZ) via atomic layer deposition (ALD) and counting the resulting zinc atoms with high sensitivity-low energy ion scattering (HS-LEIS). A bare, clean native oxide - terminated silicon wafer has 3.66 OH/nm2, which agrees with density functional theory (DFT) calculations from the literature. After TDMAMS modification of native oxide-terminated silicon, the number of surface silanols decreases by a factor of ca. 10 (to 0.31 OH/nm2). Intermediate surface testing (IST) was used to characterize the surface activities of functionalized capillaries. It suggested a significant deactivation/passivation of the capillary with some surface silanols remaining; the modified capillary shows significant deactivation compared to the native/unmodified fused silica tubing. We believe that this methodology for determining the number of residual silanols on silanized fused silica will be enabling for chromatography.

Area-Selective Atomic Layer Deposition of ZnO on Si\SiO2 Modified with Tris(dimethylamino)methylsilane.

Delayed atomic layer deposition (ALD) of ZnO, i.e., area selective (AS)-ALD, was successfully achieved on silicon wafers (Si\SiO2) terminated with tris(dimethylamino)methylsilane (TDMAMS). This resist molecule was deposited in a home-built, near-atmospheric pressure, flow-through, gas-phase reactor. TDMAMS had previously been shown to react with Si\SiO2 in a single cycle/reaction and to drastically reduce the number of silanols that remain at the surface. ZnO was deposited in a commercial ALD system using dimethylzinc (DMZ) as the zinc precursor and H2O as the coreactant. Deposition of TDMAMS was confirmed by spectroscopic ellipsometry (SE), X-ray photoelectron spectroscopy (XPS), and wetting. ALD of ZnO, including its selectivity on TDMAMS-terminated Si\SiO2 (Si\SiO2\TDMAMS), was confirmed by in situ multi-wavelength ellipsometry, ex situ SE, XPS, and/or high-sensitivity/low-energy ion scattering (HS-LEIS). The thermal stability of the TDMAMS resist layer, which is an important parameter for AS-ALD, was investigated by heating Si\SiO2\TDMAMS in air and nitrogen at 330 °C. ALD of ZnO takes place more readily on Si\SiO2\TDMAMS heated in the air than in N2, suggesting greater damage to the surface heated in the air. To better understand the in situ ALD of ZnO on Si\SiO2\TDMAMS and modified (thermally stressed) forms of it, the ellipsometry results were plotted as the normalized growth per cycle. Even one short pulse of TDMAMS effectively passivates Si\SiO2. TDMAMS can be a useful, small-molecule inhibitor of ALD of ZnO on Si\SiO2 surfaces.

The Often-Overlooked Power of Summary Statistics in Exploratory Data Analysis: Comparison of Pattern Recognition Entropy (PRE) to Other Summary Statistics and Introduction of Divided Spectrum-PRE (DS-PRE).

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X4, which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing data-they are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the "critical pair," which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.