Dental Materials最新文献

{"title":"Influence of digital crown design software on morphology, occlusal characteristics, fracture force and marginal fit","authors":"Alexander Broll ,&nbsp;Sebastian Hahnel ,&nbsp;Markus Goldhacker ,&nbsp;Jakob Rossel ,&nbsp;Michael Schmidt ,&nbsp;Martin Rosentritt","doi":"10.1016/j.dental.2025.09.003","DOIUrl":"10.1016/j.dental.2025.09.003","url":null,"abstract":"<div><h3>Objectives</h3><div>The study evaluated the influence of digital design software on crown morphology, occlusal characteristics, fracture force, and marginal fit across varying preparation designs for an identical target tooth.</div></div><div><h3>Methods</h3><div>A resin-based tooth (tooth 36) was digitized, manufactured (<span><math><mrow><mi>n</mi><mo>=</mo><mn>8</mn></mrow></math></span>), individually prepared and re-digitized. Five design groups were established using conventional software proposals, technician designs, two AI-based software solutions, and natural tooth-based reference designs. All systems employed consistent parameters. Crown designs were digitally assessed using quantitative morphological and occlusal metrics in reference to the original tooth. Crowns were milled, marginal fit was measured via digital microscopy, and fracture resistance was determined after thermal cycling and mechanical loading.</div></div><div><h3>Results</h3><div>Morphological metrics revealed statistically significant deviations across groups, with the technician design achieving the best performance. Occlusal metrics showed high deviations in the positional accuracy of the contact points across all groups. Technician and AI-based designs exhibited comparable functional results. None of the design groups were able to achieve contact with all relevant antagonist teeth, due to high deviations in the mesiolingual cusp. Conventional software designs exhibited the lowest fracture forces. Significant improvements were achieved through technician intervention. Vertical marginal discrepancies remained comparable across groups.</div></div><div><h3>Significance</h3><div>Improved functional and morphological design combined with high fracture resistance can reduce the need for clinical adjustments, minimize wear, and enhance crown longevity. Digital design software significantly influences crown morphology, occlusal characteristics and fracture forces. Vertical marginal discrepancies remain similar. AI-driven approaches demonstrate comparability with technician designs in terms of fracture forces, functional performance, and marginal fit.</div></div>","PeriodicalId":298,"journal":{"name":"Dental Materials","volume":"42 1","pages":"Pages 8-15"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145063048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gas diffusion-mediated single-sided in situ gradient mineralized silk fibroin membrane for enhanced guided bone regeneration","authors":"Zhao Li ,&nbsp;Ying Kong ,&nbsp;Qun Zhang ,&nbsp;Jing Han ,&nbsp;Kezheng Chen ,&nbsp;Baojin Ma","doi":"10.1016/j.dental.2025.09.017","DOIUrl":"10.1016/j.dental.2025.09.017","url":null,"abstract":"<div><div>Traditional guided bone regeneration (GBR) membranes face challenges in balancing mechanical strength, bioactivity, and osteoconductivity for effective periodontal bone regeneration. While collagen-based GBR membranes dominate clinical use, the weak mechanical properties and lack of osteoinductive capacity limit regeneration efficacy. Here, we presented a gas diffusion-mediated single-sided mineralization strategy to fabricate silk fibroin (SF)-based GBR membranes with dual barrier/osteoinductive functions. SF was dissolved in formic acid with Ca^2 + and, optionally, other bioactive metal ions (BMIs, such as Sr²⁺, Cu²⁺, or Mg²⁺), and a colloid was formed after the evaporation of formic acid. Followed by gradient mineralization under CO₂/NH₃ atmosphere and β-sheet induction via ethanol treatment, SF-Ca/X (X refers to other BMIs) membranes were prepared. Mineralized SF membranes featured a dense, mineral-free side for mechanical support and barrier, and an osteoinductive side by releasing BMIs. Interestingly, the calcium phosphate layer formed on the mineralized side, and the phase of CaCO₃ changed from calcite to vaterite, which helps phosphate mineralization. In vitro results demonstrated that the SF-Ca/Sr membrane enhanced osteogenic differentiation by upregulating BMP2/SMAD1 expression. In a rat mandibular defect model, the SF-Ca/Sr membrane significantly promotes new bone regeneration and collagen formation. Overall, this gas diffusion-mediated single-sided gradient mineralization approach integrates barrier properties with localized bioactivity, allowing for the required barrier/osteoinduction functions in the GBR process in one membrane.</div></div>","PeriodicalId":298,"journal":{"name":"Dental Materials","volume":"42 1","pages":"Pages 157-167"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of food-simulating liquids on the mechanical properties of lithium aluminosilicate glass-ceramics","authors":"Hanan Al-Johani ,&nbsp;Ashraf Al-Amoudi ,&nbsp;Adolfo Di Fiore ,&nbsp;Yu Zhang","doi":"10.1016/j.dental.2025.09.015","DOIUrl":"10.1016/j.dental.2025.09.015","url":null,"abstract":"<div><h3>Objectives</h3><div>To evaluate the impact of simulated aging with food-simulating liquids (FSLs) on the Martens hardness, indentation depth, flexural strength, and reliability of lithium aluminosilicate glass-ceramics.</div></div><div><h3>Methods</h3><div>Sixty square plates (12 ×12 ×1.5 mm) were prepared from a machinable fully crystallized lithium aluminosilicate glass-ceramic (Cerec Tessera, CT), then randomly allotted to four FSL groups: artificial saliva (CT-AS), citric acid (CT-CA), ethanol (CT-ET), or heptane (CT-HP). Martens hardness (HM) and indentation depth (ID) data were obtained using a hardness tester. Biaxial flexural strength (σ) was determined using the ball-on-three-balls apparatus in a universal testing machine. Weibull analysis was used to determine the characteristic strength (σ₀) and reliability (m̂_U). Data for HM and σ were analysed by one-way ANOVA, Tukey’s HSD, and Pearson correlations (α = 0.05).</div></div><div><h3>Results</h3><div>FSL type had a significant effect on HM (<em>p</em> &lt; 0.001, η_p² = 0.889), ID (<em>p</em> &lt; 0.001, η_p² = 0.879), and σ (<em>p</em> &lt; 0.001, η_p² = 0.623). Minimal differences were observed between the effects of artificial saliva and heptane on HM (<em>p</em> = 0.914), whereas citric acid (<em>p</em> &lt; 0.001) and ethanol (<em>p</em> = 0.001) showed significantly different effects. The highest σ₀ and m̂_U values were found in CT-AS (σ₀ = 319.26 MPa, m̂_U = 10.79), while the lowest were observed in CT-CA. A positive correlation was confirmed between HM and σ (<em>p</em> &lt; 0.001, r = 0.731).</div></div><div><h3>Significance</h3><div>Fully crystallized machinable lithium aluminosilicates exhibited adequate hardness and flexural strength after accelerated aging in artificial saliva; conversely, prolonged exposure to acidic FSLs jeopardized their mechanical properties.</div></div>","PeriodicalId":298,"journal":{"name":"Dental Materials","volume":"42 1","pages":"Pages 138-144"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topological features of lithium disilicate glass-ceramics uncovered through materials informatics","authors":"Satoshi Yamaguchi ,&nbsp;Hefei Li ,&nbsp;Naoya Funayama ,&nbsp;Tomoki Kohno ,&nbsp;Satoshi Imazato","doi":"10.1016/j.dental.2025.09.004","DOIUrl":"10.1016/j.dental.2025.09.004","url":null,"abstract":"<div><h3>Objective</h3><div>The aim of this study was to inversely predict the topological features underlying SEM images from arbitrary biaxial flexural strengths of glass-ceramics by Materials Informatics (MI) approach.</div></div><div><h3>Methods</h3><div>The scanning electron microscopic (SEM) image and in vitro biaxial flexural strength of 10 commercially available/experimental glass-ceramics were collected. The total of 200 SEM images were prepared as input data. Topological features underlying the SEM images were extracted using persistent homology analysis and compressed using principal component analysis. Gaussian mixture regression was employed to develop a machine learning model for predicting biaxial flexural strength based on the topological features. Arbitrary biaxial flexural strengths (390, 411, 442, 478, 515, 564, 597, 610, and 640 MPa) were defined, and an inverse analysis was conducted with the constructed machine learning model to overlay topological features onto SEM images.</div></div><div><h3>Results</h3><div>The topological features were compressed into 18 principal components. The machine learning model was selected and optimized based on the Bayesian Information Criterion. Using the constructed machine learning model, the biaxial flexural strengths were predicted with a test score of 72 % (Root Mean Squared Error: 53.5, Mean Absolute Error: 40.3). From the arbitrary biaxial flexural strengths, topological features were inversely predicted and overlaid onto SEM images.</div></div><div><h3>Conclusion</h3><div>The inverse analysis established in this study successfully predicted the topological features on SEM images of glass-ceramics from the biaxial flexural strengths. The MI approach with the inverse analysis promises to make the process to develop glassceramics more time-efficient than the conventional in vitro approach</div></div>","PeriodicalId":298,"journal":{"name":"Dental Materials","volume":"42 1","pages":"Pages 16-22"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145058116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of ceria-yttria pigmentation on the mechanical performance and esthetics of zirconia dental restorations","authors":"Sivaranjani Gali ,&nbsp;Akshay Arjun ,&nbsp;Suhasini Gururaja","doi":"10.1016/j.dental.2025.09.014","DOIUrl":"10.1016/j.dental.2025.09.014","url":null,"abstract":"<div><h3>Background</h3><div>An optimum combination of esthetics and mechanical properties is expected of all-ceramic restorations. Consequently, various pigmentation techniques of zirconia have been recommended to enhance the aesthetic results without compromising their long-term survival<strong>.</strong></div></div><div><h3>Methodology</h3><div>Infiltrate solutions of ceria-yttria were prepared by mixing their precursors in various concentrations. Pre-sintered zirconia samples were soaked in the infiltrate solutions and sintered according to the manufacturer’s instructions. Phase analysis, microstructure using scanning electron microscopy, flexural strength, fatigue, CIE Lab, translucency parameter, surface roughness, and aging resistance of infiltrated zirconia were evaluated.</div></div><div><h3>Results</h3><div>Phase analysis confirmed the presence of the tetragonal phase of zirconia, and the microstructure revealed increased grain size. The flexural strength of infiltrated zirconia ranged from 248 MPa to 512 MPa, and the fatigue limit was lower than control zirconia with reduced surface roughness. The monoclinic content before and after aging was not detectable in the infiltrated samples. The CIE Lab values of the infiltrated samples showed a trend of decreasing lightness, accompanied by higher delta E values, with minimal change in translucency.</div></div><div><h3>Conclusions</h3><div>Ceria-yttria infiltrated zirconia exhibited reasonable flexural strength and fatigue performance, with improved aging resistance, and color suitable for anterior and low-stress-bearing monolithic restorations.</div></div>","PeriodicalId":298,"journal":{"name":"Dental Materials","volume":"42 1","pages":"Pages 145-156"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LSTM-based prediction of wear in 3D-printed restorative materials under various methods","authors":"Anastasiia Grymak ,&nbsp;Alexander Hui Xiang Yang ,&nbsp;Kai Chun Li ,&nbsp;Sunyoung Ma","doi":"10.1016/j.dental.2025.09.012","DOIUrl":"10.1016/j.dental.2025.09.012","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to develop and validate a machine learning-based predictive model for forecasting wear loss in additively manufactured (AM) dental resin materials using Long Short-Term Memory (LSTM) recurrent neural networks.</div></div><div><h3>Materials and Methods</h3><div>Wear data were collected from three wear testing methods: Ball-on-Disc (BoD), Block-on-Ring (BoR), and Reciprocation (Recip), using three different AM resin materials under varying loads (49 N, 70 N) and surface treatments (polished, glazed). The LSTM model was trained on standardized time-series wear data using both Leave-One-Material-Out (LOMO) and Leave-One-Group-Out (LOGO) cross-validation strategies. Prediction windows were assessed at 10 %, 20 %, and 30 % of total wear sequences, simulating early-stage prediction of long-term wear progression. Model performance was evaluated using RMSE (Root-Mean-Square Error), MSE (Mean-Square Error), and MAE (Mean-Average Error).</div></div><div><h3>Results</h3><div>The autoregressive LSTM forecasting approach accurately predicted wear progression across all testing methods, with prediction accuracies ranging between 82 % and 97 % depending on method and dataset, the models explaining approximately 82–97 % of the wear variability (depending on method and dataset). Predictions initiated at 10 % showed high cross-validation accuracy, while test set generalization improved when prediction started at 30 %. Optimal model performance was achieved using a 50-point input window and step size. The model demonstrated robustness in handling the inherent variability of experimental wear data across multiple AM materials and test conditions.</div></div><div><h3>Significance</h3><div>This study demonstrates the feasibility of applying LSTM models for early and accurate prediction of wear progression in AM dental materials, offering potential for reducing physical testing duration and enhancing data-driven material evaluation frameworks for clinical applications.</div></div>","PeriodicalId":298,"journal":{"name":"Dental Materials","volume":"42 1","pages":"Pages 91-99"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145147137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting restoration failures in primary and permanent teeth – A machine learning approach","authors":"Vitor Henrique Digmayer Romero ,&nbsp;Eduardo Trota Chaves ,&nbsp;Shankeeth Vinayahalingam ,&nbsp;Helena Silveira Schuch ,&nbsp;Xiongjie Chen ,&nbsp;Yunpeng Li ,&nbsp;Falk Schwendicke ,&nbsp;Mariana Minatel Braga ,&nbsp;Daniela Prócida Raggio ,&nbsp;Cácia Signori ,&nbsp;Raiza Dias Freitas ,&nbsp;Fausto Medeiros Mendes ,&nbsp;Marie-Charlotte Huysmans ,&nbsp;Maximiliano Sérgio Cenci","doi":"10.1016/j.dental.2025.09.009","DOIUrl":"10.1016/j.dental.2025.09.009","url":null,"abstract":"<div><h3>Objective</h3><div>Machine learning (ML) predictive models promise to handle complex data and deliver accurate predictions in the medical field. The aim of this study was to develop ML predictive models for posterior dental restorations failures in both primary and permanent teeth.</div></div><div><h3>Methods</h3><div>Data from two clinical datasets were used in this study, encompassing a Randomized Controlled Trial (RCT) for permanent teeth (CaCIA Trial) and a corresponding RCT for primary teeth (CARDEC 3). Models were developed using five different algorithms—Decision Tree, Random Forest, XGBoost, CatBoost and Neural Network—ensuring thorough cross-validation and calibration for predictive reliability. Clinical variables related to patients and teeth were considered as predictors. Model performances were assessed using accuracy, precision, recall, F1-score and ROC AUC, alongside SHAP plots for interpretability.</div></div><div><h3>Results</h3><div>In the primary teeth dataset, all models demonstrated acceptable performance with AUC values around 0.67–0.75 and a balanced trade-off between precision and recall. In contrast, the models applied to permanent teeth yielded less predictive ability, with AUC values ranging from 0.53 to 0.62.</div></div><div><h3>Conclusion</h3><div>Our results highlight how ML approaches effectively process intricate, multi-dimensional data related to restoration longevity, successfully integrating variables across patient characteristics, tooth properties, and diagnostic assessments within a unified analytical framework. Though promising as analytical tools, clinical implementation requires further validation with expanded, heterogeneous datasets to improve robustness and accuracy.</div></div><div><h3>Clinical significance</h3><div>Machine-learning models that predict the risk of posterior restoration failure—using routinely collected patient, tooth, and diagnostic data—may help dentists tailor recall intervals, prioritize preventive or reparative care, and allocate chair time more efficiently.</div></div>","PeriodicalId":298,"journal":{"name":"Dental Materials","volume":"42 1","pages":"Pages 100-108"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polyacrylic acid/citrate/amorphous calcium phosphate complex for dentin remineralization and bond durability","authors":"Yinying Chen ,&nbsp;Xinyu Yang ,&nbsp;Suqin Zhang ,&nbsp;Hanjiao Wang ,&nbsp;Haifeng Xie ,&nbsp;Chen Chen","doi":"10.1016/j.dental.2025.09.016","DOIUrl":"10.1016/j.dental.2025.09.016","url":null,"abstract":"<div><h3>Objective</h3><div>Dentin bionic remineralization is an effective strategy for enhancing the stability of the resin-dentin bonding interface. Conventional biomimetic mineralization methods still face limitations such as restricted applicability and low mineralization efficiency. Citrate, present at high levels in biological mineralized tissues, plays a significant role in biomineralization. This study prepared polyacrylic acid/citrate/amorphous calcium phosphate complexes (PAA-Cit-ACP) and investigated its ability to promote biomimetic mineralization and improve the stability of the resin-dentin bonding interface.</div></div><div><h3>Methods</h3><div>Four types of PAA-Cit-ACP complexes, each doped with different contents of citrate (PAA-Cit-ACP-0.5, PAA-Cit-ACP-1, PAA-Cit-ACP-2, and PAA-Cit-ACP-5), were synthesized and characterized. Molecular dynamics simulation was used to clarify the mechanism behind the formation of the PAA-Cit-ACP complexes. Single-layer recombinant collagen fibers and demineralized dentin slices were constructed as mineralization models to validate the mineralization potential of PAA-Cit-ACP. Nanoleakage and in situ zymography were used to evaluate the effect of PAA-Cit-ACP on the durability of resin dentin bonding.</div></div><div><h3>Results</h3><div>Each group of PAA-Cit-ACP manifested as negatively charged, amorphous spherical nanoparticles with good biocompatibility. After treatment with PAA-Cit-ACP, both single-layer recombinant collagen fibers and demineralized dentin slices demonstrated rapid mineralization, and the resin-dentin bonding interface showed reduced nanoleakage and MMP activity, with PAA-Cit-ACP-1 and PAA-Cit-ACP-2 showing better effectiveness.</div></div><div><h3>Significance</h3><div>These findings suggest that PAA-Cit-ACP promotes rapid biomimetic remineralization, protecting exposed demineralized collagen fibrils from water- and MMPs-induced degradation, and improving the stability of the hybrid layer.</div></div>","PeriodicalId":298,"journal":{"name":"Dental Materials","volume":"42 1","pages":"Pages 126-137"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison between the dual-exponential and autocatalytic models to examine rapid photopolymerization kinetics of dental resins","authors":"B.D. MacNeil ,&nbsp;A.P. Gareau ,&nbsp;J.A.G. Guarneri ,&nbsp;R.B. Price ,&nbsp;D. Labrie","doi":"10.1016/j.dental.2025.08.015","DOIUrl":"10.1016/j.dental.2025.08.015","url":null,"abstract":"<div><h3>Objectives</h3><div>1) To investigate two empirical models used to characterize the polymerization kinetics of six resin-based composites (RBCs) and 2) the impact of the sampling rate on the time-varying degree of conversion (DC).</div></div><div><h3>Methods</h3><div>The DC of three sculptable and three flowable RBCs was recorded using attenuated total internal reflectance Fourier transform infrared spectroscopy at a collection rate of 13 DC/s. A multiple-diode light-curing unit delivered either an irradiance of 1.2 or 3 W/cm². The RBC specimens were either 0.2 mm or 4 mm thick and were photocured at 32 ºC. Sampling rates as low as 0.2 DC/s were simulated by numerically interpolating the measured DC(<em>t</em>). The DC(<em>t</em>) obtained at different sampling rates was fitted to the dual-exponential and autocatalytic models.</div></div><div><h3>Results</h3><div>For all six RBCs, the fit of the autocatalytic model to the data resulted in the smallest mean squared errors. The lower simulated sampling rates did not represent the highly time-resolved DC collected at an irradiance of 3 W/cm² and specimen thickness of 0.2 mm. For the DC simulated at a sampling rate of 0.2 DC/s using PowerFill and analyzed with the autocatalytic model, the maximum DC rate was 3.7 %/s, occurring at a time of 5 s after the start of photocuring. However, using a sampling rate of 13 DC/s, they were 64.5 %/s and 116 ms, respectively.</div></div><div><h3>Significance</h3><div>The autocatalytic model was found to better characterize the kinetics of RBC photopolymerization than the dual-exponential model. The data collection rate has a strong influence on the results.</div></div>","PeriodicalId":298,"journal":{"name":"Dental Materials","volume":"42 1","pages":"Pages 41-51"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145147163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of fused deposition modeling parameters on the mechanical and thermal properties of 3D-printed PEEK dental endosseous implants","authors":"Surendrasingh Y. Sonaye ,&nbsp;Karim Elhattab ,&nbsp;Luci R. Duncan ,&nbsp;Sai R. Dharmavarapu ,&nbsp;Vasudev Vivekanand Nayak ,&nbsp;Erfan Noorbakhsh Noshahri ,&nbsp;Nishitraj C. Sherigar ,&nbsp;Josiah S. Owusu-Danquah ,&nbsp;Lukasz Witek ,&nbsp;Marco C. Bottino ,&nbsp;Prabaha Sikder","doi":"10.1016/j.dental.2025.09.013","DOIUrl":"10.1016/j.dental.2025.09.013","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aims to explore the application of Fused Deposition Modeling (FDM) as a 3D printing technique for developing endosseous Polyetheretherketone (PEEK) dental implants. Specifically, the primary aim of the study is to systematically investigate the effects of key FDM processing parameters, including thermal conditions, print speed, layer height, build orientation, and post-processing heat treatments, on the mechanical and thermal properties of PEEK implants. By conducting an in-depth analysis, this study aims to establish optimized processing guidelines for the reliable manufacturing of high-performance, clinically viable PEEK dental implants.</div></div><div><h3>Methods</h3><div>PEEK dental implants were fabricated using FDM with variations in thermal conditions (nozzle, bedplate, and chamber temperatures), print speed, layer height, build orientation, and post-print heat treatments. Mechanical testing (compression and fatigue), detailed thermal characterization using Differential Scanning Calorimetry (DSC), and fractographic analysis were performed. Finite Element Analysis (FEA) was also conducted to understand the implant's load-bearing performance.</div></div><div><h3>Results</h3><div>Nozzle temperature dictates implant resolution, while chamber temperature is a key determinant of implant crystallinity. Interestingly, for PEEK dental implants, all the FDM thermal processing conditions play a crucial role in influencing the part's thermal properties. Moreover, print speed plays an essential role in developing dimensionally accurate high-strength implants. Notably, the fractographic analysis of the failed implants revealed interesting multimodal fracture behavior specific to 3D-printed threaded implants. FEA demonstrates that the implants tend to buckle under load and break at the implant-abutment interface, consistent with experimental results. Furthermore, fatigue testing reveals that PEEK implants, fabricated at a specific build orientation with respect to the bedplate, suffice the Food and Drug Administration durability requirements.</div></div><div><h3>Significance</h3><div>These findings underscore the clinical potential of FDM-developed PEEK as a customizable, lightweight, and durable alternative to conventional metallic implants, paving the way for next-generation patient-specific lightweight dental implant solutions.</div></div>","PeriodicalId":298,"journal":{"name":"Dental Materials","volume":"42 1","pages":"Pages 109-125"},"PeriodicalIF":6.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}