Study of semi-synthetic plastic objects of historic interest using non-invasive total reflectance FT-IR

A significant proportion of modern and contemporary artifacts and objects of historical interest, are composed of materials in the form of synthetic, semi-synthetic, and natural polymers. Each class of polymer and corresponding composite plastics are subject to different degradation processes. This means that conservators and curators of 20th century collections are faced with varied, nontrivial preservation issues. An unresolved problem is the identification of early plastics based on semi-synthetic polymers such as cellulose nitrate, cellulose acetate, and casein formaldehyde, which were often used to imitate the more valuable natural materials such as ivory, tortoiseshell, ebony, and bone. This exemplifies the need for non-invasive methods specifically tailored for identification of plastic materials in collections, so as to provide conservators with a means of materials classification to support preventive conservation strategies and interventive treatments. The present work is aimed at evaluating the effectiveness of non-invasive Total Reflectance (TR) FT-IR spectroscopy, coupled with a custom reference spectral TR FT-IR library, for the identification of materials comprising a series of unknown objects. A set of ten heritage objects made from early semi-synthetic materials was used as a training test set to validate the proposed methodological approach. The FT-IR data acquired on the test objects were pre-processed and finally classified using commercial software tools used for the automatic classification of spectra in FT-IR spectroscopy. The procedure was successfully applied to several cases, although residual uncertainties remained in a few examples. The results obtained are critically analyzed and discussed in the perspective of proposing a robust method for in-field prescreening of the chemical composition of plastic artistic and historical objects

Anti-angiogenic and anti-proliferative graphene oxide nanosheets for tumor cell therapy

Graphene oxide (GO) is a bidimensional novel material that exhibits high biocompatibility and angiogenic properties, mostly related to the intracellular formation of reactive oxygen species (ROS). In this work, we set up an experimental methodology for the fabrication of GO@peptide hybrids by the immobilization, via irreversible physical adsorption, of the Ac-(GHHPH)4-NH2 peptide sequence, known to mimic the anti-angiogenic domain of the histidine-proline-rich glycoprotein (HPRG). The anti-proliferative capability of the graphene-peptide hybrids were tested in vitro by viability assays on prostate cancer cells (PC-3 line), human neuroblastoma (SH-SY5Y), and human retinal endothelial cells (primary HREC). The anti-angiogenic response of the two cellular models of angiogenesis, namely endothelial and prostate cancer cells, was scrutinized by prostaglandin E2 (PGE2) release and wound scratch assays, to correlate the activation of inflammatory response upon the cell treatments with the GO@peptide nanocomposites to the cell migration processes. Results showed that the GO@peptide nanoassemblies not only effectively induced toxicity in the prostate cancer cells, but also strongly blocked the cell migration and inhibited the prostaglandin-mediated inflammatory process both in PC-3 and in HRECs. Moreover, the cytotoxic mechanism and the internalization efficiency of the theranostic nanoplatforms, investigated by mitochondrial ROS production analyses and confocal microscopy imaging, unraveled a dose-dependent manifold mechanism of action performed by the hybrid nanoassemblies against the PC-3 cells, with the detection of the GO-characteristic cell wrapping and mitochondrial perturbation. The obtained results pointed out to the very promising potential of the synthetized graphene-based hybrids for cancer therapy

Graphene Oxide Nanosheets Tailored With Aromatic Dipeptide Nanoassemblies for a Tuneable Interaction With Cell Membranes

Engineered graphene-based derivatives are attractive and promising candidates for nanomedicine applications because of their versatility as 2D nanomaterials. However, the safe application of these materials needs to solve the still unanswered issue of graphene nanotoxicity. In this work, we investigated the self-assembly of dityrosine peptides driven by graphene oxide (GO) and/or copper ions in the comparison with the more hydrophobic diphenylalanine dipeptide. To scrutinize the peptide aggregation process, in the absence or presence of GO and/or Cu2+, we used atomic force microscopy, circular dichroism, UV–visible, fluorescence and electron paramagnetic resonance spectroscopies. The perturbative effect by the hybrid nanomaterials made of peptide-decorated GO nanosheets on model cell membranes of supported lipid bilayers was investigated. In particular, quartz crystal microbalance with dissipation monitoring and fluorescence recovery after photobleaching techniques were used to track the changes in the viscoelastic properties and fluidity of the cell membrane, respectively. Also, cellular experiments with two model tumour cell lines at a short time of incubation, evidenced the high potential of this approach to set up versatile nanoplatforms for nanomedicine and theranostic applications

On the Perspectives of Image-to-Lidar Constraints in Dynamic Network Optimisation

The evolution of airborne mapping witnesses the introduction of hybrid lidar-camera systems to enhance data collection, i.e. to obtain simultaneously high-density point-cloud and texture. Yet, the common adjustment of both optical data streams is a non-trivial problem due to challenges associated with the different influences of errors affecting their mapping accuracy including those coming from navigation sensors. Stemming from a special form of graph-based optimization, the dynamic networks allow rigorous modeling of spatio-temporal constraints and thus provide the common framework for optimizing original observations from inertial systems with those coming from optical sensors. In this work, we propose a cross-domain observation model that leverages pixel-to-point correspondences as links between imagery and lidar returns. First, we describe how the existence of such correspondences can be introduced into optimizations. Second, we employ a reference dataset to emulate a set of precise pixel-to-point correspondences to assess its prospective impact on the common (rather than cascade) optimization. We report the improvement in the estimated trajectory attitude error with lower quality IMU and thus the point-cloud registration. Finally, we study whether such correspondences could be contained from freely available deep learning networks with the desired accuracy and quality. We conclude that although the introduction of such camera-to-lidar constraints has significant potential, none of the studied machine learning networks can fulfill the requirement on correspondence detection in terms of quality