A strategy for creating a general-purposes surface functionalization platform is reported, based on direct attachment of phosphate groups onto hydroxylated surfaces and subsequent formation of a terpyridine-based monolayer. Such a platform is suitable for the construction, onto technologically relevant oxide surfaces, of single- and multilayer structures of interest in technological applications. In particular, the paper describes the successful attachment of 4-(2,2':6',2''-terpyridine-4-yl)benzenephosphonic acid (1, PPTP) onto a SiO(2) surface previously functionalized by means of Zr-phosphate groups. Two alternative anchoring strategies of the PPTP were explored: (i) a direct one-step way, implying no protection of terpyridinic functionality, and (ii) a three-step way, implying protection and successive deprotection of this group. It was found that, in the first case, the PPTP ligand anchoring to the Zr-containing phosphate layer takes place by means of terpyridinic group. At variance of this, in the second case, due to the protection of the terpyridinic functionality, the anchoring process takes place through the phosphonic group, making the terpyridinic moiety available for further reactions, i.e., multilayer constructs. X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) were used to study the functionalized surfaces, providing information on coverage, chemical structure, and stoichiometry of the various functionalized layers and, among the others, clear evidence of the PPTP linkage and orientation.

NO-dosing provides a tool for extending the applicability, in SIMS depth profiling, of the widely spread fullerene ion sources. In view of the acceptable erosion rates on inorganics, obtainable with C60, the method could be of relevance also in connection with the 3D-imaging of hybrid polymer/inorganic systems.

Fluorescent carbon quantum dots (CDs) are synthesized and employed as fluorescent nanochemosensors for selective detection of amino acids. A detailed investigation of excitation−emission maps revealed that the fluorescence properties of CDs are intensely and strongly influenced by the interaction at the surface with different amino acids. The discrimination capability was demonstrated by tensor rank decomposition of the differences induced by the surface reaction in the excitation−emission maps and by means of a common machine learning approach based on artificial neural networks.

The continuum equation is used for modeling erosion rate, ion beam induced mixing, and reactions during the sputtering process involved in secondary ion mass spectrometry experiments. We developed a new approach that is able to incorporate the beam induced reactivity, so leading to a reasonable simulation of depth profiles of polymers and organic solids. The model allows one to include the effects of the reactive gas dosing on sputtering yield and damage accumulation during profiling. Comparison with experimental data confirms the quality of the model and strengthens the proposed approach.

A detailed depth characterization of multilayered polymeric systems is a very attractive topic. Currently, the use of cluster primary ion beams in time-of-flight secondary ion mass spectrometry allows molecular depth profiling of organic and polymeric materials. Because typical raw data may contain thousands of peaks, the amount of information to manage grows rapidly and widely, so that data reduction techniques become indispensable in order to extract the most significant information from the given dataset. Here, we show how the wavelet-based signal processing technique can be applied to the compression of the giant raw data acquired during time-of-flight secondary ion mass spectrometry molecular depth-profiling experiments. We tested the approach on data acquired by analyzing a model sample consisting of polyelectrolyte-based multilayers spin-cast on silicon. Numerous wavelet mother functions and several compression levels were investigated. We propose some estimators of the filtering quality in order to find the highest 'safe' approximation value in terms of peaks area modification, signal to noise ratio, and mass resolution retention. The compression procedure allowed to obtain a dataset straightforwardly 'manageable' without any peak-picking procedure or detailed peak integration. Moreover, we show that multivariate analysis, namely, principal component analysis, can be successfully combined to the results of the wavelet-filtering, providing a simple and reliable method for extracting the relevant information from raw datasets.