The chemical composition and band gap of amorphous Si:C:N:H layers

Structural and optical properties o plasma-deposited a-C:H:Si:O:N films

“…The optical gap, shown as a function of R ox in Figure 10, falls from ~ 3.3 to ~2.3 eV as R ox is increased. This interval overlaps with that of 1.87 and 2.7 eV reported by Swatowska [8] for a-C:H:Si:N films deposited from CH 4 , SiH 4 e NH 3 . Greater nitrogen or carbon contents tend to increase the gap [8] .…”

“…This interval overlaps with that of 1.87 and 2.7 eV reported by Swatowska [8] for a-C:H:Si:N films deposited from CH 4 , SiH 4 e NH 3 . Greater nitrogen or carbon contents tend to increase the gap [8] . Thus, the decline in [C] and [N] with increasing R ox may account, at least in part, for the observed fall in the gap.…”

“…Thin films of a-C:H:Si:N [1][2][3][4][5][6][7][8] , a-C:H:Si:O [9][10][11][12][13][14][15][16][17][18][19][20] , and a-C:H:Si:O:N [21][22][23][24] , where a designates amorphous, have been produced by plasma deposition from diverse monomers and comonomers. For example, a-C:H:Si:N has been produced, among others, in plasmas fed hexamethyldisilazane and nitrogen [1] , diethylsilane and ammonia [3] , and methane, silane and nitrogen [5] .…”

Structural, Optical and AC Conductivity Studies on Polycrystalline-Si/Nanocrystalline-FeSi2 Composite Thin Films

“…Here we focus on Si/β-FeSi2 composite thin films, which contain nanocrystalline β-FeSi2 particles within Si matrix [3,4]. Crystalline Si (c-Si) has an indirect bandgap (Eg ind ) of 1.12 eV [5], while the amorphous Si (a-Si) has Eg ind values of about 1.4-1.8 eV [6,7]. β-FeSi2 also exhibits semiconducting properties with a direct band-gap (Eg dir ) of 0.87 eV and Eg ind values of 0.765 eV [8,9].…”

Partial Least-Squares Regression as a Tool To Predict Fluoropolymer Surface Modification by Dielectric Barrier Discharge in a Corona Process Configuration in a Nitrogen–Organic Gaseous Precursor Environment

“…[6][7][8][9][10][11] Basically, these researches were based on extensive studies requiring several experiments where each of the plasma parameters of interest were modified independently while trying to correlate their influence with the coating surface chemistry using analytical techniques such as x-ray photoelectron spectroscopy (XPS), Fourier Transform infrared spectroscopy (FTIR), or time-of-flight secondary ion mass spectroscopy (TOF-SIMS). [12][13][14][15][16][17] Despite having brought a lot of information, such an approach is time consuming and often leads to results that are difficult to interpret or simply contradictory. 18,19 In this context, Partial Least Squares Regression (PLSR) was identified as a tool that allows to unambiguously evidence relationships between plasma experimental parameters and surface coatings chemistry.…”