Machine Learning-Enabled Tomographic Imaging of Chemical Short-Range Atomic Ordering.

In solids, chemical short-range order (CSRO) refers to the self-organization of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. Yet quantitative relationships between properties and the morphology, number density, and atomic configurations of CSRO domains remain elusive. Herein, it is showcased how machine learning-enhanced atom probe tomography (APT) can mine the near-atomically resolved APT data and jointly exploit the technique's high elemental sensitivity to provide a 3D quantitative analysis of CSRO in a CoCrNi medium-entropy alloy. Multiple CSRO configurations are revealed, with their formation supported by state-of-the-art Monte-Carlo simulations. Quantitative analysis of these CSROs allows establishing relationships between processing parameters and physical properties. The unambiguous characterization of CSRO will help refine strategies for designing advanced materials by manipulating atomic-scale architectures.

Ceramic Implant Rehabilitation: Consensus Statements from Joint Congress for Ceramic Implantology: Consensus Statements on Ceramic Implant.

The objectives of the study group focused on the following main topics related to the performance of 1- and 2-piece ceramic implants: defining bone-implant-contact percentages and its measurement methods, evaluating the pink esthetic score as an esthetic outcome parameter after immediate implantation, recognizing the different results of ceramic implant designs as redefined by the German Association of Oral Implantology, incorporating the patient report outcome measure to include satisfaction and improvement in oral health-related quality of life, and conducting preclinical studies to address existing gaps in ceramic implants. During the Joint Congress for Ceramic Implantology (2022), the study group evaluated 17 clinical trials published between 2015 and 2021. After extensive discussions and multiple closed sessions, consensus statements and recommendations were developed, incorporating all approved modifications. A 1-piece implant design features a coronal part that is fused to the implant body or interfaces with the postabutment restoration platform, undergoing transmucosal healing. Long-term evaluations of this implant design are supported by established favorable clinical evidence. Inaccuracies in the pink esthetic score and bone-implant-contact percentages were managed by establishing control groups for preclinical studies and randomizing clinical trials. The patient-reported outcome measures were adjusted to include an individual visual analog scale, collected from each clinical study, that quantified improved oral health and quality of life. Preclinical investigations should focus on examining the spread of ceramic debris and the impact of heat generation on tissue and cellular levels during drilling. Further technical advancements should prioritize wound management and developing safe drilling protocols.

Quasi-"In Situ" Analysis of the Reactive Liquid-Solid Interface during Magnesium Corrosion Using Cryo-Atom Probe Tomography.

The early stages of corrosion occurring at liquid-solid interfaces control the evolution of the material's degradation process, yet due to their transient state, their analysis remains a formidable challenge. Here corrosion tests are performed on a MgCa alloy, a candidate material for biodegradable implants using pure water as a model system. The corrosion reaction is suspended by plunge freezing into liquid nitrogen. The evolution of the early-stage corrosion process on the nanoscale by correlating cryo-atom probe tomography (APT) with transmission-electron microscopy (TEM) and spectroscopy, is studied. The outward growth of Mg hydroxide Mg(OH)2 and the inward growth of an intermediate corrosion layer consisting of hydrloxides of different compositions, mostly monohydroxide Mg(OH) instead of the expected MgO layer, are observed. In addition, Ca partitions to these newly formed hydroxides and oxides. Density-functional theory calculations suggest a domain of stability for this previously experimental unreported Mg(OH) phase. This new approach and these new findings advance the understanding of the early stages of magnesium corrosion, and in general reactions and processes at liquid-solid interfaces, which can further facilitate the development of corrosion-resistant materials or better control of the biodegradation rate of future implants.

Comparative analysis of surface phase diagrams in aqueous environment: Implicit vs explicit solvation models.

Identifying the stable surface phases under a given electrochemical conditions serves as the basis for studying the atomistic mechanism of reactions at solid/water interfaces. In this work, we systematically compare the performance of the two main approaches that are used to capture the impact of an aqueous environment, implicit and explicit solvent, on surface energies and phase diagrams. As a model system, we consider the magnesium/water interface with (i) Ca substitution and (ii) proton and hydroxyl adsorption. We show that while the implicit solvent model is computationally very efficient, it suffers from two shortcomings. First, the choice of the implicit solvent parameters significantly influences the energy landscape in the vicinity of the surface. The default parameters benchmarked on solvation in water underestimate the energy of the dissolved Mg ion and lead to spontaneous dissolution of the surface atom, resulting in large differences in the surface energetics. Second, in systems containing a charged surface and a solvated ion, the implicit solvent model may not converge to the energetically stable ionic charge state but remain in a high-energy metastable configuration, representing the neutral charge state of the ion. When these two issues are addressed, surface phase diagrams that closely match the explicit water results can be obtained. This makes the implicit solvent model highly attractive as a computationally-efficient surrogate model to compute surface energies and phase diagrams.

Laterally Resolved Free Energy Profiles and Vibrational Spectra of Chemisorbed H Atoms on Pt(111).

A scheme to compute laterally resolved free energy surfaces and spectral signatures of specifically adsorbed ions on electrode surfaces from their ab initio molecular dynamics (AIMD) trajectories is proposed. Considering H-covered Pt(111) electrodes, both in contact with water and vacuum and for various H coverages, we systematically explore the impact of explicit water and H-coverage on site occupancy, providing direct insight into the proportion of underpotential and overpotential deposited hydrogen adsorbates. Extending this approach further, we can obtain laterally resolved vibrational spectra of the Pt-H stretch modes. We discuss how the difference between the free energy basins of the on-top and fcc-hollow adsorption sites explains the features of the experimentally observed spectral fingerprints in this system. These fingerprints do not contain only information about the stable and metastable adsorption sites but also about intermediate short-lived adsorbate configurations. Our results also show that for these properties chemisorbed H2O acts as a spectator and does not qualitatively influence the relative stabilities of the adsorption sites and their spectral fingerprints.

Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography.

Chemical short-range order (CSRO) refers to atoms of specific elements self-organising within a disordered crystalline matrix to form particular atomic neighbourhoods. CSRO is typically characterized indirectly, using volume-averaged or through projection microscopy techniques that fail to capture the three-dimensional atomistic architectures. Here, we present a machine-learning enhanced approach to break the inherent resolution limits of atom probe tomography enabling three-dimensional imaging of multiple CSROs. We showcase our approach by addressing a long-standing question encountered in body-centred-cubic Fe-Al alloys that see anomalous property changes upon heat treatment. We use it to evidence non-statistical B2-CSRO instead of the generally-expected D03-CSRO. We introduce quantitative correlations among annealing temperature, CSRO, and nano-hardness and electrical resistivity. Our approach is further validated on modified D03-CSRO detected in Fe-Ga. The proposed strategy can be generally employed to investigate short/medium/long-range ordering phenomena in different materials and help design future high-performance materials.

A Machine Learning Framework for Quantifying Chemical Segregation and Microstructural Features in Atom Probe Tomography Data.

Atom probe tomography (APT) is ideally suited to characterize and understand the interplay of segregation and microstructure in modern multi-component materials. Yet, the quantitative analysis typically relies on human expertise to define regions of interest. We introduce a computationally efficient, multi-stage machine learning strategy to identify compositionally distinct domains in a semi-automated way, and subsequently quantify their geometric and compositional characteristics. In our algorithmic pipeline, we first coarse-grain the APT data into voxels, collect the composition statistics, and decompose it via clustering in composition space. The composition classification then enables the real-space segmentation via a density-based clustering algorithm, thus revealing the microstructure at voxel resolution. Our approach is demonstrated for a Sm-(Co,Fe)-Zr-Cu alloy. The alloy exhibits two precipitate phases with a plate-like, but intertwined morphology. The primary segmentation is further refined to disentangle these geometrically complex precipitates into individual plate-like parts by an unsupervised approach based on principle component analysis, or a U-Net-based semantic segmentation trained on the former. Following the composition and geometric analysis, detailed composition distribution and segregation effects relative to the predominant plate-like geometry can be readily mapped from the point cloud, without resorting to the voxel compositions.

Enhancing corrosion-resistant alloy design through natural language processing and deep learning.

We propose strategies that couple natural language processing with deep learning to enhance machine capability for corrosion-resistant alloy design. First, accuracy of machine learning models for materials datasets is often limited by their inability to incorporate textual data. Manual extraction of numerical parameters from descriptions of alloy processing or experimental methodology inevitably leads to a reduction in information density. To overcome this, we have developed a fully automated natural language processing approach to transform textual data into a form compatible for feeding into a deep neural network. This approach has resulted in a pitting potential prediction accuracy substantially beyond state of the art. Second, we have implemented a deep learning model with a transformed-input feature space, consisting of a set of elemental physical/chemical property-based numerical descriptors of alloys replacing alloy compositions. This helped identification of those descriptors that are most critical toward enhancing their pitting potential. In particular, configurational entropy, atomic packing efficiency, local electronegativity differences, and atomic radii differences proved to be the most critical.

Implant-Abutment Connections and Their Effect on Implant Survival Rates and Changes in Marginal Bone Levels (Δ): A Systematic Review and Meta-Analysis of 45,347 Oral Implants.

Purpose: To quantify the cumulative oral implant survival rates and changes in radiographic bone levels based on the configuration of the implant-abutment connection type over time. Materials and Methods: An electronic literature search was conducted in four databases (PubMed/MEDLINE, Cochrane Library, Web of Science, and Embase), and records were refereed by two independent reviewers based on the inclusion criteria. Data from included articles were grouped by implant-abutment connection type into four categories ([1] external hex; [2] bone level, internal, narrow cone < 45 degrees; [3] bone level, internal wide cone ≥ 45 degrees or flat; and [4] tissue level) and duration of follow-up (short-term 1 to 2 years, mid-term 2 to 5 years, and long-term > 5 years). Meta-analyses were performed for cumulative survival rate (CSR) and changes in marginal bone level (ΔMBL) from baseline (loading) to last reported follow-up. Studies were split or merged as appropriate based on the implants and follow-up duration in the study and trial design. The study was compiled under PRISMA 2020 guidelines and registered in the PROSPERO database. Results: A total of 3,082 articles were screened. Full-text review of 465 articles resulted in a total of 270 articles (representing 16,448 subjects with 45,347 implants) included for quantitative synthesis and analysis. Mean ΔMBL (95% CI) was as follows: short-term external hex = 0.68 mm (0.57, 0.79); short-term bone level, internal, narrow cone < 45 degrees = 0.34 mm (0.25, 0.43); short-term bone level, internal wide cone ≥ 45 degrees = 0.63 mm (0.52, 0.74); short-term tissue level = 0.42 mm (0.27, 0.56); mid-term external hex = 1.03 mm (0.72, 1.34); mid-term bone level, internal, narrow cone < 45 degrees = 0.45 mm (0.34, 0.56); mid-term bone level, internal wide cone ≥ 45 degrees = 0.73 mm (0.58, 0.88); mid-term tissue level = 0.4 mm (0.21, 0.61); long-term external hex = 0.98 mm, 0.70, 1.25); long-term bone level, internal, narrow cone < 45 degrees = 0.44 mm (0.31, 0.57); long-term bone level, internal wide cone ≥ 45 degrees = 0.95 mm (0.68, 1.22); and long-term tissue level = 0.43 mm (0.24, 0.61). CSRs (95% CI) were: short-term external hex = 97% (96%, 98%); short-term bone level, internal, narrow cone < 45 degrees = 99% (99%, 99%); short-term bone level, internal wide cone ≥ 45 degrees = 98% (98%, 99%); short-term tissue level = 99% (98%, 100%); mid-term external hex = 97% (96%, 98%); mid-term bone level, internal, narrow cone < 45 degrees = 98% (98%, 99%); mid-term bone level, internal wide cone ≥ 45 degrees = 99% (98%, 99%); mid-term tissue level = 98% (97%, 99%); long-term external hex = 96% (95%, 98%); long-term bone level, internal, narrow cone < 45 degrees = 98% (98%, 99%); long-term bone level, internal wide cone ≥ 45 degrees = 99% (98%, 100%); and long-term tissue level = 99% (98%, 100%). Conclusion: The configuration of the implant-abutment interface has a measurable effect on the ΔMBL over time. These changes can be observed over a period of at least 3 to 5 years. At all measured time intervals, similar ΔMBL was noted for external hex and internal wide cone ≥ 45-degree connections, as were internal, narrow cone < 45-degree and tissue-level connections.

Ceramic Dental Implants: A Systematic Review and Meta-analysis.

Purpose: To evaluate the performance of one- and two-piece ceramic implants regarding implant survival and success and patient satisfaction. Materials and Methods: This review followed the PRISMA 2020 guidelines using PICO format and analyzed clinical studies of partially or completely edentulous patients. The electronic search was conducted in PubMed/MEDLINE using Medical Subject Headings (MeSH) keywords related to dental zirconia ceramic implants, and 1,029 records were received for detailed screening. The data obtained from the literature were analyzed by single-arm, weighted meta-analyses using a random-effects model. Forest plots were used to synthesize pooled means and 95% CI for the change in marginal bone level (MBL) for short-term (1 year), mid-term (2 to 5 years), and long-term (over 5 years) follow-up time intervals. Results: Among the 155 included studies, the case reports, review articles, and preclinical studies were analyzed for background information. A meta-analysis was performed for 11 studies for one-piece implants. The results indicated that the MBL change after 1 year was 0.94 ± 0.11 mm, with a lower bound of 0.72 and an upper bound of 1.16. For the mid term, the MBL was 1.2 ± 0.14 mm with a lower bound of 0.92 and an upper bound of 1.48. For the long term, the MBL change was 1.24 ± 0.16 mm with a lower bound of 0.92 and an upper bound of 1.56. Conclusion: Based on this literature review, one-piece ceramic implants achieve osseointegration similar to titanium implants, with a stable MBL or a slight bone gain after an individual initial design depending on crestal remodeling. The risk of implant fracture is low for current commercially available implants. Immediate loading or temporization of the implants does not interfere with the course of osseointegration. Scientific evidence for two-piece implants is rare.

Morphological Studies to Identify the Nasopalatine and Inferior Alveolar Nerve Using a Special Head and Neck MRI Coil.

OBJECTIVES: Procedures in oral and maxillofacial surgery bear a high risk of nerve damage. Three-dimensional imaging techniques can optimize surgical planning and help to spare nerves. The aim of this study was to investigate the diagnostic value of a 1.5 T magnetic resonance imaging (MRI) scanner with a dedicated dental signal amplification coil for the assessment of nerves in the oral cavity as compared with cone beam computed tomography (CBCT). METHODS: Based on 6 predefined criteria, the assessability of the inferior alveolar and nasopalatine nerves in CBCT and MRI with a dedicated 4-channel dental coil were compared in 24 patients. RESULTS: Compared with CBCT, MRI with the dental coil showed significantly better evaluability of the inferior alveolar nerve in the sagittal and axial plane and the nasopalatine nerve in the axial plane. In the sagittal plane; however, the assessability of the nasopalatine nerve was significantly better in CBCT as compared with MRI. Yet, pertaining to overall assessability, no significant differences between modalities were found. CONCLUSIONS: In this pilot study, it can be reported that 1.5- T MRI with a dedicated dental coil is at least equivalent, if not superior, to CBCT in imaging nerve structures of the stomatognathic system. CLINICAL RELEVANCE: Preoperative, 3-dimensional images are known to simplify and refine the planning and execution of operations in maxillofacial surgery. In contrast to computed tomography and CBCT, MRI does not cause radiation exposure while enabling visualization of all relevant hard and soft tissues and, therefore, holds an advantage over well-established techniques.

Accelerating the design of compositionally complex materials via physics-informed artificial intelligence.

The chemical space for designing materials is practically infinite. This makes disruptive progress by traditional physics-based modeling alone challenging. Yet, training data for identifying composition-structure-property relations by artificial intelligence are sparse. We discuss opportunities to discover new chemically complex materials by hybrid methods where physics laws are combined with artificial intelligence.

Simulating short-range order in compositionally complex materials.

In multicomponent materials, short-range order (SRO) is the development of correlated arrangements of atoms at the nanometer scale. Its impact in compositionally complex materials has stimulated an intense debate within the materials science community. Understanding SRO is critical to control the properties of technologically relevant materials, from metallic alloys to functional ceramics. In contrast to long-range order, quantitative characterization of the nature and spatial extent of SRO evades most of the experimentally available techniques. Simulations at the atomistic scale have full access to SRO but face the challenge of accurately sampling high-dimensional configuration spaces to identify the thermodynamic and kinetic conditions at which SRO is formed and what impact it has on material properties. Here we highlight recent progress in computational approaches, such as machine learning-based interatomic potentials, for quantifying and understanding SRO in compositionally complex materials. We briefly recap the key theoretical concepts and methods.

Machine learning-enabled high-entropy alloy discovery.

High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10-6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties.

Inflammation of the Human Dental Pulp Induces Phosphorylation of eNOS at Thr495 in Blood Vessels.

The activity of endothelial nitric oxide synthase (eNOS) in endothelial cells increased with the phosphorylation of the enzyme at Ser1177 and decreased at Thr495. The regulation of the phosphorylation sites of eNOS at Ser1177 and Thr495 in blood vessels of the healthy and inflamed human dental pulp is unknown. To investigate this, healthy and carious human third molars were immersion-fixed and decalcified. The localization of eNOS, Ser1177, and Thr495 in healthy and inflamed blood vessels was examined in consecutive cryo-sections using quantitative immunohistochemical methods. We found that the staining intensity of Ser1177 in healthy blood vessels decreased in inflamed blood vessels, whereas the weak staining intensity of Thr495 in healthy blood vessels strongly increased in inflamed blood vessels. In blood vessels of the healthy pulp, eNOS is active with phosphorylation of the enzyme at Ser1177. The phosphorylation of eNOS at Thr495 in inflamed blood vessels leads to a decrease in eNOS activity, contributing to eNOS uncoupling and giving evidence for a decrease in NO and an increase in O2- production. Since the formation of the tertiary dentin matrix depends on intact pulp circulation, eNOS uncoupling and phosphorylation of eNOS at Thr495 in the inflamed pulp blood vessels should be considered during caries therapy.

Controlled Doping of Electrocatalysts through Engineering Impurities.

Fuel cells recombine water from H2 and O2 thereby can power, for example, cars or houses with no direct carbon emission. In anion-exchange membrane fuel cells (AEMFCs), to reach high power densities, operating at high pH is an alternative to using large volumes of noble metals catalysts at the cathode, where the oxygen-reduction reaction occurs. However, the sluggish kinetics of the hydrogen-oxidation reaction (HOR) hinders upscaling despite promising catalysts. Here, the authors observe an unexpected ingress of B into Pd nanocatalysts synthesized by wet-chemistry, gaining control over this B-doping, and report on its influence on the HOR activity in alkaline conditions. They rationalize their findings using ab initio calculations of both H- and OH-adsorption on B-doped Pd. Using this "impurity engineering" approach, they thus design Pt-free catalysts as required in electrochemical energy conversion devices, for example, next generations of AEMFCs, that satisfy the economic and environmental constraints, that is, reasonable operating costs and long-term stability, to enable the "hydrogen economy."

Immediate Implant Placement in Conjunction with Acellular Dermal Matrix or Connective Tissue Graft: A Randomized Controlled Clinical Volumetric Study.

Connective tissue grafts have become a standard for compensating horizontal volume loss in immediate implant placement. The use of new biomaterials like acellular matrices may avoid the need to harvest autogenous grafts, yielding less postoperative morbidity. This randomized comparative study evaluated the clinical outcomes following extraction and immediate implant placement in conjunction with anorganic bovine bone mineral (ABBM) and the use of a porcine acellular dermal matrix (ADM) vs an autogenous connective tissue graft (CTG) in the anterior maxilla. Twenty patients (11 men, 9 women) with a mean age of 48.9 years (range: 21 to 72 years) were included in the study and randomly assigned to either the test (ADM) or control (CTG) group. They underwent tooth extraction and immediate implant placement together with ABBM for socket grafting and either ADM or CTG for soft tissue augmentation. Twelve months after implant placement, the cases were evaluated clinically and volumetrically. All implants achieved osseointegration and were restored. The average horizontal change of the ridge dimension at 1 year postsurgery was -0.55 ± 0.32 mm for the ADM group and -0.60 ± 0.49 mm for the CTG group. Patients of the ADM group reported significantly less postoperative pain. Using xenografts for hard and soft tissue augmentation in conjunction with immediate implant placement showed no difference in the volume change in comparison to an autogenous soft tissue graft, and showed significantly less postoperative morbidity.

Clinical performance of zirconia implant abutments luted to a titanium base - a retrospective cross-sectional study.

AIM: To evaluate the survival of implant-retained restorations fabricated on CAD/CAM-derived zirconia abutments luted to a titanium base. MATERIALS AND METHODS: 153 patients who received a total of 310 dental implants (Camlog Promote plus or Xive S) and all-ceramic restorations on yttria-stabilized tetragonal zirconia polycrystal (3Y-TZP) abutments luted to a titanium base during the last 10 years were included. Patients were examined for technical complications during routine visits. Crestal bone level changes were randomly analyzed based on periapical radiographs of 75 implants. RESULTS: Among the included 153 patients, 17 ceramic chippings (5.5%), 6 abutment loosenings (1.9%), and 2 abutment fractures (0.6%) were identified. The mean follow-up time was 4.7 years (standard deviation [SD]: 1.94), with a follow-up period of up to 10 years (maximum). Kaplan-Meier estimation resulted in a survival rate without complications of 91.6% for the restoration and 97.4% for the abutment. There was no statistically significant difference between the two implant systems, either between implant location or regarding the complication rate of the type of restoration. For the 75 implants included in the radiographic analysis, the mean bone level change was 0.384 mm (SD: 0.242, 95% CI: 0.315 to 0.452) for the Camlog implant system and 0.585 mm (SD: 0.366, 95% CI: 0.434 to 0.736) for the Xive system (P = 0.007). CONCLUSION: The results of the present retrospective study demonstrate acceptable clinical outcomes for zirconia abutments luted to a titanium base in combination with all-ceramic restorations. The assessed abutment design does not appear to have a negative impact on peri-implant hard tissue.

Understanding Alkali Contamination in Colloidal Nanomaterials to Unlock Grain Boundary Impurity Engineering.

Metal nanogels combine a large surface area, a high structural stability, and a high catalytic activity toward a variety of chemical reactions. Their performance is underpinned by the atomic-level distribution of their constituents, yet analyzing their subnanoscale structure and composition to guide property optimization remains extremely challenging. Here, we synthesized Pd nanogels using a conventional wet chemistry route, and a near-atomic-scale analysis reveals that impurities from the reactants (Na and K) are integrated into the grain boundaries of the poly crystalline gel, typically loci of high catalytic activity. We demonstrate that the level of impurities is controlled by the reaction condition. Based on ab initio calculations, we provide a detailed mechanism to explain how surface-bound impurities become trapped at grain boundaries that form as the particles coalesce during synthesis, possibly facilitating their decohesion. If controlled, impurity integration into grain boundaries may offer opportunities for designing new nanogels.