Antibacterial and clinical effectiveness of a mouthwash with a novel active system of amine + zinc lactate + fluoride: a randomized controlled trial.

OBJECTIVES: To support the daily oral hygiene of patients experiencing gum inflammation, a new mouthwash was developed containing an amine + zinc lactate + fluoride system. In vitro and clinical efficacy was assessed using traditional methods as well as using novel site-specific and subject-specific analyses of the clinical data. MATERIALS AND METHODS: This mouthwash was evaluated in a 12-h biofilm regrowth assay against a negative control mouthwash and in a 6-month plaque and gingivitis clinical study as compared to a negative control mouthwash. Analyses of healthy versus inflamed sites, visible plaque versus non-visible plaque sites, as well as subject-level evaluations bring new perspectives to the overall performance of this mouthwash and its significance from a patient outcome perspective. RESULTS: Studies demonstrated that this new mouthwash provided long-term (12-h) antibacterial activity after single application in vitro and reduced clinically all plaque and gingivitis parameters after 3 months and 6 months of use when compared to the negative control mouthwash. Examination of site-level and subject-level data determined that this mouthwash significantly increased the number of healthy sites in the oral cavity and significantly improved the gum health of subjects in the study, as compared to the negative control mouthwash. CONCLUSIONS: In vitro and clinical research has demonstrated the antibacterial and clinical benefits of this mouthwash containing an amine compound + zinc lactate + fluoride system. CLINICAL RELEVANCE: Our subject-specific and site-specific analyses provide the dental practitioner with tools that can be used to guide patients who suffer from gingivitis toward optimal product selection and use. CLINICAL TRIAL REGISTRATION: The trial was registered at ClinicalTrials.gov (reference no. NCT05821712).

A Review of Non-Destructive Testing (NDT) Techniques for Defect Detection: Application to Fusion Welding and Future Wire Arc Additive Manufacturing Processes.

In Wire and Arc Additive Manufacturing (WAAM) and fusion welding, various defects such as porosity, cracks, deformation and lack of fusion can occur during the fabrication process. These have a strong impact on the mechanical properties and can also lead to failure of the manufactured parts during service. These defects can be recognized using non-destructive testing (NDT) methods so that the examined workpiece is not harmed. This paper provides a comprehensive overview of various NDT techniques for WAAM and fusion welding, including laser-ultrasonic, acoustic emission with an airborne optical microphone, optical emission spectroscopy, laser-induced breakdown spectroscopy, laser opto-ultrasonic dual detection, thermography and also in-process defect detection via weld current monitoring with an oscilloscope. In addition, the novel research conducted, its operating principle and the equipment required to perform these techniques are presented. The minimum defect size that can be identified via NDT methods has been obtained from previous academic research or from tests carried out by companies. The use of these techniques in WAAM and fusion welding applications makes it possible to detect defects and to take a step towards the production of high-quality final components.

Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions.

Within the fields of materials mechanics, the consideration of physical laws in machine learning predictions besides the use of data can enable low prediction errors and robustness as opposed to predictions only based on data. On the one hand, exclusive utilization of fundamental physical relationships might show significant deviations in their predictions compared to reality, due to simplifications and assumptions. On the other hand, using only data and neglecting well-established physical laws can create the need for unreasonably large data sets that are required to exhibit low bias and are usually expensive to collect. However, fundamental but simplified physics in combination with a corrective model that compensates for possible deviations, e.g., to experimental data, can lead to physics-based predictions with low prediction errors, also despite scarce data. In this article, it is demonstrated that a hybrid model approach consisting of a physics-based model that is corrected via an artificial neural network represents an efficient prediction tool as opposed to a purely data-driven model. In particular, a semi-analytical model serves as an efficient low-fidelity model with noticeable prediction errors outside its calibration domain. An artificial neural network is used to correct the semi-analytical solution towards a desired reference solution provided by high-fidelity finite element simulations, while the efficiency of the semi-analytical model is maintained and the applicability range enhanced. We utilize residual stresses that are induced by laser shock peening as a use-case example. In addition, it is shown that non-unique relationships between model inputs and outputs lead to high prediction errors and the identification of salient input features via dimensionality analysis is highly beneficial to achieve low prediction errors. In a generalization task, predictions are also outside the process parameter space of the training region while remaining in the trained range of corrections. The corrective model predictions show substantially smaller errors than purely data-driven model predictions, which illustrates one of the benefits of the hybrid modelling approach. Ultimately, when the amount of samples in the data set is reduced, the generalization of the physics-related corrective model outperforms the purely data-driven model, which also demonstrates efficient applicability of the proposed hybrid modelling approach to problems where data is scarce.

A Strategy for Dimensionality Reduction and Data Analysis Applied to Microstructure-Property Relationships of Nanoporous Metals.

Nanoporous metals, with their complex microstructure, represent an ideal candidate for the development of methods that combine physics, data, and machine learning. The preparation of nanporous metals via dealloying allows for tuning of the microstructure and macroscopic mechanical properties within a large design space, dependent on the chosen dealloying conditions. Specifically, it is possible to define the solid fraction, ligament size, and connectivity density within a large range. These microstructural parameters have a large impact on the macroscopic mechanical behavior. This makes this class of materials an ideal science case for the development of strategies for dimensionality reduction, supporting the analysis and visualization of the underlying structure-property relationships. Efficient finite element beam modeling techniques were used to generate ~200 data sets for macroscopic compression and nanoindentation of open pore nanofoams. A strategy consisting of dimensional analysis, principal component analysis, and machine learning allowed for data mining of the microstructure-property relationships. It turned out that the scaling law of the work hardening rate has the same exponent as the Young's modulus. Simple linear relationships are derived for the normalized work hardening rate and hardness. The hardness to yield stress ratio is not limited to 1, as commonly assumed for foams, but spreads over a large range of values from 0.5 to 3.

Giant electrochemical actuation in a nanoporous silicon-polypyrrole hybrid material.

The absence of piezoelectricity in silicon makes direct electromechanical applications of this mainstream semiconductor impossible. Integrated electrical control of the silicon mechanics, however, would open up new perspectives for on-chip actuorics. Here, we combine wafer-scale nanoporosity in single-crystalline silicon with polymerization of an artificial muscle material inside pore space to synthesize a composite that shows macroscopic electrostrain in aqueous electrolyte. The voltage-strain coupling is three orders of magnitude larger than the best-performing ceramics in terms of piezoelectric actuation. We trace this huge electroactuation to the concerted action of 100 billions of nanopores per square centimeter cross section and to potential-dependent pressures of up to 150 atmospheres at the single-pore scale. The exceptionally small operation voltages (0.4 to 0.9 volts), along with the sustainable and biocompatible base materials, make this hybrid promising for bioactuator applications.

A Review of Experimentally Informed Micromechanical Modeling of Nanoporous Metals: From Structural Descriptors to Predictive Structure-Property Relationships.

Nanoporous metals made by dealloying take the form of macroscopic (mm- or cm-sized) porous bodies with a solid fraction of around 30%. The material exhibits a network structure of "ligaments" with an average ligament diameter that can be adjusted between 5 and 500 nm. Current research explores the use of nanoporous metals as functional materials with respect to electrochemical conversion and storage, bioanalytical and biomedical applications, and actuation and sensing. The mechanical behavior of the network structure provides the scope for fundamental research, particularly because of the high complexity originating from the randomness of the structure and the challenges arising from the nanosized ligaments, which can be accessed through an experiment only indirectly via the testing of the macroscopic properties. The strength of nanoscale ligaments increases systematically with decreasing size, and owing to the high surface-to-volume ratio their elastic and plastic properties can be additionally tuned by applying an electric potential. Therefore, nanoporous metals offer themselves as suitable model systems for exploring the structure-property relationships of complex interconnected microstructures as well as the basic mechanisms of the chemo-electro-mechanical coupling at interfaces. The micromechanical modeling of nanoporous metals is a rapidly growing field that strongly benefits from developments in computational methods, high-performance computing, and visualization techniques; it also benefits at the same time through advances in characterization techniques, including nanotomography, 3D image processing, and algorithms for geometrical and topological analysis. The review article collects articles on the structural characterization and micromechanical modeling of nanoporous metals and discusses the acquired understanding in the context of advancements in the experimental discipline. The concluding remarks are given in the form of a summary and an outline of future perspectives.

Numerical Investigation of Polymer Coated Nanoporous Gold.

Nanoporous metals represent a fascinating class of materials. They consist of a bi-continuous three-dimensional network of randomly intersecting pores and ligaments where the ligaments form the skeleton of the structure. The open-pore structure allows for applying a thin electrolytic coating on the ligaments. In this paper, we will investigate the stiffening effect of a polymer coating numerically. Since the coating adds an additional difficulty for the discretization of the microstructure by finite elements, we apply the finite cell method. This allows for deriving a mesh in a fully automatic fashion from the high resolution 3D voxel model stemming from the 3D focused ion beam-scanning electron microscope tomography data of nanoporous gold. By manipulating the voxel model in a straightforward way, we add a thin polymer layer of homogeneous thickness numerically and study its effect on the macroscopic elastic properties systematically. In order to lower the influence of the boundary conditions on the results, the window method, which is known from homogenization procedures, is applied. In the second part of the paper, we fill the gap between numerical simulations and experimental investigations and determine real material properties of an electrolytic applied polypyrrole coating by inverse computations. The simulations provide an estimate for the mechanical properties of the ligaments and the polymeric coating and are in accordance with experimental data.

Quantification of the Vulcanizing System of Rubber in Industrial Tire Rubber Production by Laser-Induced Breakdown Spectroscopy (LIBS).

The properties of natural and synthetic rubber critically depend on the concentration of the vulcanizing system, among others. Sulfur and zinc oxide are typically used as cross-linking and activating agents for the vulcanization reaction (0-3 wt %). We present an advanced spectroscopic method to chemically analyze the vulcanizing system in rubber under ambient conditions, and we demonstrate a novel application to measure the elements in-line of industrial rubber production. The laser-induced breakdown spectroscopy (LIBS) technique is optimized to ablate material from the surface of produced rubber sheets and to measure the optical emission of S and Zn from the rubber plasma in air. The sulfur lines in the near-infrared range are masked by molecular emission bands of the C-N radical and spectrally interfered by atomic lines of O. Plasma excitation in collinear double-pulse geometry and detection of plasma emission with time-gated detectors suppresses the spectroscopic overlays and enables to resolve the sulfur lines. For the determination of ZnO the weak Zn lines in the ultraviolet range are measured due to their superior intensity stability compared to the much stronger lines in the deeper UV. S and ZnO are quantified in three different rubber materials prepared from the most important polymers used in rubber production. The mean error of prediction of concentrations RMSEP is ≤0.07 wt % for S and ≤0.33 wt % for ZnO for all polymer types. Our results demonstrate that the vulcanizing system of rubber can be quantified under ambient conditions with LIBS. Other chemical elements could be analyzed also and the rubber production could be controlled employing this multielement detection technique as process analytical sensor.

Plastic Poisson's Ratio of Nanoporous Metals: A Macroscopic Signature of Tension-Compression Asymmetry at the Nanoscale.

The suggestion, based on atomistic simulation, of a surface-induced tension-compression asymmetry of the strength and flow stress of small metal bodies so far lacks experimental confirmation. Here, we present the missing experimental evidence. We study the transverse plastic flow of nanoporous gold under uniaxial compression. Performing mechanical tests in electrolyte affords control over the surface state. Specifically, the surface tension, γ, can be varied in situ during plastic flow. We find that decreasing γ leads to an increase of the effective macroscopic plastic Poisson ratio, νP. Finite element simulations of a network with surface tension confirm the notion that νP of nanoporous gold provides a signature for a local tension-compression asymmetry of the nanoscale struts that form the network. We show that γ promotes compression while impeding tensile elongation. Because the transverse strain is partly carried by the elongation of ligaments oriented normal to the load axis, the surface-induced tension-compression asymmetry acts to reduce νP. Our experiment confirms a decisive contribution of the surface tension to small-scale plasticity.

Magnesium degradation as determined by artificial neural networks.

Magnesium degradation under physiological conditions is a highly complex process in which temperature, the use of cell culture growth medium and the presence of CO2, O2 and proteins can influence the corrosion rate and the composition of the resulting corrosion layer. Due to the complexity of this process it is almost impossible to predict the parameters that are most important and whether some parameters have a synergistic effect on the corrosion rate. Artificial neural networks are a mathematical tool that can be used to approximate and analyse non-linear problems with multiple inputs. In this work we present the first analysis of corrosion data obtained using this method, which reveals that CO2 and the composition of the buffer system play a crucial role in the corrosion of magnesium, whereas O2, proteins and temperature play a less prominent role.

Hierarchical flexural strength of enamel: transition from brittle to damage-tolerant behaviour.

Hard, biological materials are generally hierarchically structured from the nano- to the macro-scale in a somewhat self-similar manner consisting of mineral units surrounded by a soft protein shell. Considerable efforts are underway to mimic such materials because of their structurally optimized mechanical functionality of being hard and stiff as well as damage-tolerant. However, it is unclear how different hierarchical levels interact to achieve this performance. In this study, we consider dental enamel as a representative, biological hierarchical structure and determine its flexural strength and elastic modulus at three levels of hierarchy using focused ion beam (FIB) prepared cantilevers of micrometre size. The results are compared and analysed using a theoretical model proposed by Jäger and Fratzl and developed by Gao and co-workers. Both properties decrease with increasing hierarchical dimension along with a switch in mechanical behaviour from linear-elastic to elastic-inelastic. We found Gao's model matched the results very well.

Screen-printed bienzymatic sensor based on sol-gel immobilized Nippostrongylusbrasiliensis acetylcholinesterase and a cytochrome P450 BM-3 (CYP102-A1) mutant.

Here, we describe the development of a bi-enzymatic biosensor that simplifies the sample pretreatment steps for insecticide detection, and opens the way for a highly sensitive detection of phosphorothionates in food. These compounds evolve their inhibitory activity towards acetylcholinesterases (AChEs) only after oxidation, which is performed in vivo by P450 monooxygenases. Consequently, phosphorothionates require a suitable sample pretreatment by selective oxidation to be detectable in AChE based systems. In this study, enzymatic phosphorothionate activation and AChE inhibition were integrated in a single biosensor unit. A triple mutant of cytochrome P450 BM-3 (CYP 102-A1) and Nippostrongylus brasiliensis AChE (NbAChE) was immobilized using a fluoride catalyzed sol-gel process. Different sol-gel types were fabricated and characterized regarding enzyme loading capacity and enzyme activity containment. The enzyme sol-gel itself already proved to be suitable for the highly sensitive detection of paraoxon and parathion in a spectrometric assay. A method for screen-printing of this enzyme sol-gel on thick film electrodes was developed. Finally, amperometric biosensors containing coimmobilized NbAChE and the cytochrome P450 BM-3 mutant were produced and characterized with respect to signal stability, organophosphate detection, and storage stability. The detection limits achieved were 1 microg/L for paraoxon and 10 microg/L for parathion, which is according to EC regulations the highest tolerable pesticide concentration in infant food.