Absorption Properties of Large Complex Molecular Systems: The DFTB/Fluctuating Charge Approach

We report on the first formulation of a novel polarizable QM/MM approach, where the density functional tight binding (DFTB) is coupled to the fluctuating charge (FQ) force field. The resulting method (DFTB/FQ) is then extended to the linear response within the TD-DFTB framework and challenged to study absorption spectra of large condensed-phase systems

Do We Really Need Quantum Mechanics to Describe Plasmonic Properties of Metal Nanostructures?

Optical properties of metal nanostructures are the basis of several scientific and technological applications. When the nanostructure characteristic size is of the order of few nm or less, it is generally accepted that only a description that explicitly describes electrons by quantum mechanics can reproduce faithfully its optical response. For example, the plasmon resonance shift upon shrinking the nanostructure size (red-shift for simple metals, blue-shift for d-metals such as gold and silver) is universally accepted to originate from the quantum nature of the system. Here we show instead that an atomistic approach based on classical physics, ωFQFμ (frequency dependent fluctuating charges and fluctuating dipoles), is able to reproduce all the typical “quantum” size effects, such as the sign and the magnitude of the plasmon shift, the progressive loss of the plasmon resonance for gold, the atomistically detailed features in the induced electron density, and the non local effects in the nanoparticle response. To support our findings, we compare the ωFQFμ results for Ag and Au with literature time-dependent DFT simulations, showing the capability of fully classical physics to reproduce these TDDFT results. Only electron tunneling between nanostructures emerges as a genuine quantum mechanical effect, that we had to include in the model by an ad hoc term

Automated aircraft dent inspection via a modified Fourier transform profilometry algorithm

The search for dents is a consistent part of the aircraft inspection workload. The engineer is required to find, measure, and report each dent over the aircraft skin. This process is not only hazardous, but also extremely subject to human factors and environmental conditions. This study discusses the feasibility of automated dent scanning via a single-shot triangular stereo Fourier transform algorithm, designed to be compatible with the use of an unmanned aerial vehicle. The original algorithm is modified introducing two main contributions. First, the automatic estimation of the pass-band filter removes the user interaction in the phase filtering process. Secondly, the employment of a virtual reference plane reduces unwrapping errors, leading to improved accuracy independently of the chosen unwrapping algorithm. Static experiments reached a mean absolute error of ∼0.1 mm at a distance of 60 cm, while dynamic experiments showed ∼0.3 mm at a distance of 120 cm. On average, the mean absolute error decreased by ∼34%, proving the validity of the proposed single-shot 3D reconstruction algorithm and suggesting its applicability for future automated dent inspections.Cranfield IVHM Centr

QM/Classical Modeling of Surface Enhanced Raman Scattering Based on Atomistic Electromagnetic Models

We present quantum mechanics (QM)/frequency dependent fluctuating charge (QM/ωFQ) and fluctuating dipoles (QM/ωFQFμ) multiscale approaches to model surface-enhanced Raman scattering spectra of molecular systems adsorbed on plasmonic nanostructures. The methods are based on a QM/classical partitioning of the system, where the plasmonic substrate is treated by means of the atomistic electromagnetic models ωFQ and ωFQFμ, which are able to describe in a unique fashion and at the same level of accuracy the plasmonic properties of noble metal nanostructures and graphene-based materials. Such methods are based on classical physics, i.e. Drude conduction theory, classical electrodynamics, and atomistic polarizability to account for interband transitions, by also including an ad-hoc phenomenological correction to describe quantum tunneling. QM/ωFQ and QM/ωFQFμ are thus applied to selected test cases, for which computed results are compared with available experiments, showing the robustness and reliability of both approaches

Defects recognition algorithm development from visual UAV inspections

Aircraft maintenance plays a key role in the safety of air transport. One of its most significant procedures is the visual inspection of the aircraft skin for defects. This is mainly carried out manually and involves a high skilled human walking around the aircraft. It is very time consuming, costly, stressful and the outcome heavily depends on the skills of the inspector. In this paper, we propose a two-step process for automating the defect recognition and classification from visual images. The visual inspection can be carried out with the use of an unmanned aerial vehicle (UAV) carrying an image sensor to fully automate the procedure and eliminate any human error. With our proposed method in the first step, we perform the crucial part of recognizing the defect. If a defect is found, the image is fed to an ensemble of classifiers for identifying the type. The classifiers are a combination of different pretrained convolution neural network (CNN) models, which we retrained to fit our problem. For achieving our goal, we created our own dataset with defect images captured from aircrafts during inspection in TUI’s maintenance hangar. The images were preprocessed and used to train different pretrained CNNs with the use of transfer learning. We performed an initial training of 40 different CNN architectures to choose the ones that best fitted our dataset. Then, we chose the best four for fine tuning and further testing. For the first step of defect recognition, the DenseNet201 CNN architecture performed better, with an overall accuracy of 81.82%. For the second step for the defect classification, an ensemble of different CNN models was used. The results show that even with a very small dataset, we can reach an accuracy of around 82% in the defect recognition and even 100% for the classification of the categories of missing or damaged exterior paint and primer and dents

Deletions of NRXN1 (neurexin-1) predispose to a wide spectrum of developmental disorders.

Research has implicated mutations in the gene for neurexin-1 (NRXN1) in a variety of conditions including autism, schizophrenia, and nicotine dependence. To our knowledge, there have been no published reports describing the breadth of the phenotype associated with mutations in NRXN1. We present a medical record review of subjects with deletions involving exonic sequences of NRXN1. We ascertained cases from 3,540 individuals referred clinically for comparative genomic hybridization testing from March 2007 to January 2009. Twelve subjects were identified with exonic deletions. The phenotype of individuals with NRXN1 deletion is variable and includes autism spectrum disorders, mental retardation, language delays, and hypotonia. There was a statistically significant increase in NRXN1 deletion in our clinical sample compared to control populations described in the literature (P = 8.9 x 10(-7)). Three additional subjects with NRXN1 deletions and autism were identified through the Homozygosity Mapping Collaborative for Autism, and this deletion segregated with the phenotype. Our study indicates that deletions of NRXN1 predispose to a wide spectrum of developmental disorders