Ipek Efe, Alexander Vogel, William S. Huxter
et al.
Nanoscale electrostatic control of oxide interfaces enables physical phenomena and exotic functionalities beyond the realm of the bulk material. In technologically-relevant ferroelectric thin films, the interface-mediated polarization control is usually exerted by engineering the depolarizing field. Here, in contrast, we introduce polarizing surfaces and lattice chemistry engineering as an alternative strategy. Specifically, we engineer the electric-dipole ordering in ferroelectric oxide heterostructures by exploiting the charged sheets of the layered Aurivillius model system. By tracking in-situ the formation of the Aurivillius charged Bi$_{2}$O$_{2}$ sheets, we reveal their polarizing effect leading to the characteristic Aurivillius out-of-plane antipolar ordering. Next, we use the polarizing Bi$_{2}$O$_{2}$ stacking as a versatile electrostatic environment to create new electric dipole configurations. We insert multiferroic BiFeO$_3$ into the Aurivillius framework to stabilize a ferrielectric-like non-collinear electric-dipole order in the final heterostructure while maintaining the antiferromagnetic order of BiFeO$_3$. We thus demonstrate that engineering the lattice chemistry stabilizes unconventional ferroic orderings at the nanoscale, a strategy that may be expanded beyond the realm of electrically ordered materials.
Nadine Malich-Bohlig, Eva Dam-Christensen, Hildegunn Juulsgaard Johannesen
et al.
Resumé
I denne artikel undersøges lærerstuderendes anvendelse af fagsprog i egen undervisning og deres didaktiske refleksioner gennem videobaserede analyser (Blikstad-Balas, 2016; Blikstad-Balas & Jenset, 2024). QUINT-studier (Universitetet i Oslo, 2024) fremhæver, at struktureret analyse og refleksion over videooptagelser af undervisning kan styrke lærerstuderendes professionelle forståelse af undervisning. Artiklen præsenterer resultaterne af et studie, hvor sigtet var at udforske lærerstuderendes forståelse af brugen af fagsprog i deres undervisningspraksis samt deres metareflekterende gruppedialoger, når de analyserer videoer af egen undervisning. Studiet er gennemført i fagene dansk, fysik/kemi, samfundsfag og historie med det formål at undersøge de studerendes brug af fagsprog på tværs af fag. Studiet består af 16 videooptagelser af undervisningssekvenser i de fire fag og tilhørende audiooptagelser af lærerstuderendes analyser ved brug af en udviklet analyseramme og refleksion gennem gruppedialoger. Afslutningsvist diskuteres det, om lærerstuderendes videografiske analyser og metarefleksioner kan være redskaber som støtte for den studerendes fagsproglige udvikling.
Nøgleord
fagsprog, kernepraksisser, videografiske observationer
Abstract
This article examines student teachers’ use of subject-specific language in their teaching and their didactic reflections through video-based analyses. Drawing on QUINT studies (2024), it explores how structured analysis of teaching videos enhances professional understanding. The study investigates student teachers’ application of subject-specific language and meta-reflective group dialogues across Danish, physics/chemistry, social studies, and history, focusing on cross-disciplinary linguistic similarities. Data includes 16 teaching video recordings and corresponding audio-recorded analyses using an analytical framework. The discussion highlights the potential of videographic analyses and meta-reflections in fostering subject-specific language development.
Keywords
subject-specific language, Core Practices, videographic observations
Objectives:
This study assessed undergraduate dental students’ self-confidence in performing endodontic procedures and examined factors influencing their competence, including academic performance, year of study, and place of residence.
Materials and Methods:
A cross-sectional survey was conducted among dental students in Saudi Arabia using a structured questionnaire. Participants self-assessed their confidence levels in different endodontic procedures using a 5-point Likert scale. Statistical analysis included t-tests and ANOVA to evaluate associations between demographic variables and confidence levels (P < 0.05).
Results:
Students exhibited high confidence in simpler procedures, such as rubber dam placement (78.6%), but reported lower confidence in treating molars (33.3%) and handling interappointment flare-ups (51.6%). Higher GPA and clinical experience significantly improved confidence (P < 0.01). Urban students exhibited higher confidence than rural students.
Conclusion:
Enhanced curriculum modifications, simulation-based training, and clinical exposure to complex cases are essential to improve confidence levels and preparedness among dental students.
Large Language Models (LLMs) have achieved remarkable success and have been applied across various scientific fields, including chemistry. However, many chemical tasks require the processing of visual information, which cannot be successfully handled by existing chemical LLMs. This brings a growing need for models capable of integrating multimodal information in the chemical domain. In this paper, we introduce \textbf{ChemVLM}, an open-source chemical multimodal large language model specifically designed for chemical applications. ChemVLM is trained on a carefully curated bilingual multimodal dataset that enhances its ability to understand both textual and visual chemical information, including molecular structures, reactions, and chemistry examination questions. We develop three datasets for comprehensive evaluation, tailored to Chemical Optical Character Recognition (OCR), Multimodal Chemical Reasoning (MMCR), and Multimodal Molecule Understanding tasks. We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks. Experimental results demonstrate that ChemVLM achieves competitive performance across all evaluated tasks. Our model can be found at https://huggingface.co/AI4Chem/ChemVLM-26B.
<p>Oxidation of volatile organic compounds (VOCs) can lead to the formation of secondary organic aerosol (SOA), a significant component of atmospheric fine particles, which can affect air quality, human health, and climate change. However, the current understanding of the formation mechanism of SOA is still incomplete, which is not only due to the complexity of the chemistry but also relates to analytical challenges in SOA precursor detection and quantification. Recent instrumental advances, especially the development of high-resolution time-of-flight chemical ionization mass spectrometry (CIMS), greatly improved both the detection and quantification of low- and extremely low-volatility organic molecules (LVOCs/ELVOCs), which largely facilitated the investigation of SOA formation pathways. However, analyzing and interpreting complex mass spectrometric data remain a challenging task. This necessitates the use of dimension reduction techniques to simplify mass spectrometric data with the purpose of extracting chemical and kinetic information of the investigated system. Here we present an approach to apply fuzzy <span class="inline-formula"><i>c</i></span>-means clustering (FCM) to analyze CIMS data from a chamber experiment, aiming to investigate the gas phase chemistry of the nitrate-radical-initiated oxidation of isoprene.</p>
<p>The performance of FCM was evaluated and validated. By applying FCM to measurements, various oxidation products were classified into different groups, based on their chemical and kinetic properties, and the common patterns of their time series were identified, which provided insight into the chemistry of the investigated system. The chemical properties of the clusters are described by elemental ratios and the average carbon oxidation state, and the kinetic behaviors are parameterized with a generation number and effective rate coefficient (describing the average reactivity of a species) using the gamma kinetic parameterization model. In addition, the fuzziness of FCM algorithm provides a possibility for the separation of isomers or different chemical processes that species are involved in, which could be useful for mechanism development. Overall, FCM is a technique that can be applied well to simplify complex mass spectrometric data, and the chemical and kinetic properties derived from clustering can be utilized to understand the reaction system of interest.</p>
Introduction:
Patients with significant maxillary atrophy who are not candidates for standard implants now have an option thanks to zygomatic implants. Long-term statistics on difficulties and success are, however, scarce.
Methods:
A tertiary care center’s patient data were retrospectively analyzed. Included were patients who underwent zygomatic implant surgery between 2017 and 2022. This research gathered and examined data on follow-up, surgical techniques, complications, demographics, and implant features.
Findings:
There were 100 patients in all. 92% of the implants were successful, and 92 of them survived. Peri-implantitis (20%), soft tissue dehiscence (15%), sinusitis (10%), prosthesis fracture (8%), and infection (5%), were among the biological consequences. In summary, zygomatic implants have the potential to help individuals with severe maxillary atrophy recover, but close observation and effective management of any problems are necessary to maximize results. To enhance patient care and improve treatment regimens, further research is required.
Coline Bichlmaier, Antonella Di Pizio, Maik Behrens
et al.
As a global commodity with profound economic and social impact, coffee’s uniqueness is rooted in its distinctive flavor profile, characterized by roasty odors and a bitter taste. Mozambioside, a diterpene glucoside predominantly found in Arabica coffee, has emerged as a potent activator of human bitter receptors TAS2R43 and TAS2R46, exhibiting a bitterness threshold ten times lower than caffeine. The roasting process degrades mozambioside into new compounds. The roasting products were purified from model pyrolysis using liquid chromatographic techniques and their structures were elucidated and characterized by time-of-flight mass spectrometry (MS) and nuclear magnetic resonance spectroscopy. Mozambioside and its roasting products were quantified by targeted UHPLC-MS/MS in coffee powders and brews. Bitter receptor activation was investigated in HEK 293T-Gα16gust44 cells in terms of activation threshold and dose-response. Receptor activation thresholds of the major roasting products 11-<i>O</i>-β-D-glucosyl-(<i>S</i>)-16-desoxy-17-oxocafestol-2-on, 11-<i>O</i>-β-D-glucosyl-15,16-dehydrocafestol-2-on, 11-<i>O</i>-β-D-glucosyl-(<i>R</i>)-16-desoxy-17-oxocafestol-2-on, and bengalensol were lower than those of mozambioside. Molecular Modelling clarified the protein–molecule interaction. The compounds were formed during coffee roasting, reaching their maximum concentration in the final roasting grade. Quantitative analyses revealed that the degradation products were quantitatively extracted from the powder into the brew. During roasting, mozambioside undergoes degradation, giving rise to new compounds with a lower activation threshold for bitter receptors, putatively contributing to the bitterness of Arabica coffee brews. Advanced analytical techniques provide insights into the intricate chemistry underlying coffee’s unique flavor profile.
Jaspreet Kaur, Avreet Sandhu, Rupinder Kaur
et al.
Traumatic dental injuries (TDIs) have significant long-term consequences for the oral cavity's hard and soft tissues. Alveolar process fractures are particularly complicated. This case report describes the management and 12-month follow-up of a segmental maxillary alveolar process fracture involving laterally luxated primary incisors. A case of a 4-year-old boy was reported to the hospital 1 hour after an accidental fall at school, which resulted in a fracture of the maxillary alveolar process. Emergency treatment consisted of fracture reduction and repositioning of the primary incisors, followed by a semirigid splint between maxillary canines. The splints were removed at the end of week 4, and the affected primary incisors remained asymptomatic.
Shahram Seidi, Mohammad Tajik, Mahroo Baharfar
et al.
Abstract Despite the significant advances in analytical instruments, applying a sample preparation step before the final analysis has a great deal of importance. This fact is attributed to the complexity of the sample matrices and the existence of the analytes at trace levels. The recent trends in analytical chemistry are focused on downscaling and development of new sample preparation methods. Micro solid-phase extraction (pipette tip and spin column) and thin film solid-phase microextraction are among the relatively new miniaturized concepts, which have recently achieved much attention thanks to their unique advantages. In this review, these methods and their latest achievements, including possible configurations, different types of exploited commercial and synthesized sorbents, and their potential for automation and routine laboratory tasks are discussed. Finally, the chromatographic applications of these methods are investigated for the analysis of different analytes in various sample matrices.
1,8-Naphthalimide, as one of the classical dyes and fluorophores, has been widely used in analytical chemistry, materials chemistry, and biochemistry fields because of its excellent characteristic photostability, good structural flexibility, high fluorescence quantum yield, and large Stokes shift. This review mainly focuses on 1,8-naphthalimide and its derivatives in ion detection, molecular recognition, material applications, and bioimaging in the past five years. Simultaneously, we hope to develop more powerful fluorescent chemosensors for broad and exciting applications in the future.
The article is a response to a recent opinion piece that log concentration values should not be applied in analytical chemistry. An essential aim in the development of analytical chemistry methods is to obtain more sensitive and accurate detection values. For the application of chemical analysis methods, the obtained experiment data need to fit with the mathematical functions in the first place. As influenced by different detection principles and analytical methods, data can be displayed in a coordinate system with two linear axes for linear function fitting, or the data can first be taken through a logarithmic transformation and then for function fitting. Using raw data or data after logarithmic transformation primarily depends on analytical principles, without special rules of data formats. For example, ultraviolet-visible spectrophotometric data are more suitable for direct linear fitting. However, enzyme-catalyzed reaction or electrochemical data in logarithmic form are more appropriate for function fitting. This transformation of data form will not affect the soundness of fit statistics; rather, it simplifies the complexity of function analysis and calculation, which are the essence of analytical chemistry. In this brief article, we provide justification and legitimacy of the application of logarithmic processing in various fields of quantitative analytical chemistry.