Research Applications Research Articles

Functionality of macrophages encapsulated in porcine decellularized adipose matrix hydrogels and interaction with Candida albicans

Extracellular matrix hydrogels are considered one of the most suitable biomaterials for tissue regeneration due to their similarity with the extracellular microenvironment of the native tissue. Their properties are dependent on their composition, material concentration, fiber density and the fabrication approaches, among other factors. The encapsulation of immune cells in this kind of hydrogels, both in absence or presence of a pathogen, represents a promising strategy for the development of platforms that mimic healthy and infected tissues, respectively. In this work, we have encapsulated macrophages in 3D hydrogels of porcine decellularized adipose matrices (pDAMs) without and with the Candida albicans fungus, as 3D experimental models to study the macrophage immunocompetence in a closer situation to the physiological conditions and to mimic an infection scenario. Our results indicate that encapsulated macrophages preserve their functionality within these pDAM hydrogels and phagocytose live pathogens. In addition, their behavior is influenced by the hydrogel pore size, inversely related to the hydrogel concentration. Thus, larger pore size promotes the polarization of macrophages towards M2 phenotype along the time and enhances their phagocytosis capability. It is important to point out that encapsulated macrophages in absence of pathogen showed an M2 phenotype, but macrophages coencapsulated with C. albicans can switch towards an M1 inflammatory phenotype to resolve the infection, depending on the fungus quantity. The present study reveals that pDAM hydrogels preserve the macrophage plasticity, demonstrating their relevance as new models for macrophage-pathogen interaction studies that mimic an infection scenario with application in regenerative medicine research.

Linguistic and attentional factors – Not statistical regularities – Contribute to word-selective neural responses with FPVS-oddball paradigms

Studies using frequency-tagging in electroencephalography (EEG) have dramatically increased in the past 10 years, in a variety of domains and populations. Here we used Fast Periodic Visual Stimulation (FPVS) combined with an oddball design to explore visual word recognition. Given the paradigm's high sensitivity, it is crucial for future basic research and clinical application to prove its robustness across variations of designs, stimulus types and tasks. This paradigm uses periodicity of brain responses to measure discrimination between two experimentally defined categories of stimuli presented periodically. EEG was recorded in 22 adults who viewed words inserted every 5 stimuli (at 2 Hz) within base stimuli presented at 10 Hz. Using two discrimination levels (deviant words among nonwords or pseudowords), we assessed the impact of relative frequency of item repetition (set size or item repetition controlled for deviant versus base stimuli), and of the orthogonal task (focused or deployed spatial attention). Word-selective occipito-temporal responses were robust at the individual level (significant in 95% of participants), left-lateralized, larger for the prelexical (nonwords) than lexical (pseudowords) contrast, and stronger with a deployed spatial attention task as compared to the typically used focused task. Importantly, amplitudes were not affected by item repetition. These results help understanding the factors influencing word-selective EEG responses and support the validity of FPVS-EEG oddball paradigms, as they confirm that word-selective responses are linguistic. Second, they show its robustness against design-related factors that could induce statistical (ir)regularities in item rate. They also confirm its high individual sensitivity and demonstrate how it can be optimized, using a deployed rather than focused attention task, to measure implicit word recognition processes in typical and atypical populations.

Multiple Branch Deep Neural Network and Feature Fusion Model for Underwater Image Enhancement

Underwater imaging faces formidable challenges due to light absorption, scattering, and restricted visibility, demanding sophisticated enhancement methods. This research presents a novel Multiple Branch Deep Neural Network and Feature Fusion (MB-DNN-FF) Model tailored specifically for Underwater Images Enhancement (UWIE), with a focus on its application to the UFO120 dataset. Driven by the limitations of existing techniques, our model harnesses the potential of Deep Learning (DL) and Feature Fusion (FF) to effectively address the intricate complexities present in underwater environments. The innovative architecture incorporates multiple branches, each strategically designed to tackle distinct challenges such as contrast degradation, limited visibility, and color distortion. Central to our model is feature fusion, a critical aspect that harmoniously integrates information from diverse branches, thereby enhancing overall image quality. The training and optimization processes are thoroughly detailed, encompassing unique strategies and loss functions fine-tuned for the nuances of UWIE, with emphasis on the characteristics of the UFO120 dataset. Experimental evaluations leverage the comprehensive UFO120 dataset, employing established performance metrics to quantitatively assess the efficacy of our model. Results exhibit substantial improvements over baseline models and state-of-the-art methods, showcasing the effectiveness and versatility of the proposed multiple branch model, particularly on the UFO120 dataset. The discussion interprets these findings in the context of underwater imaging challenges, highlighting the model's effectiveness and outlining potential applications. This research contributes a valuable asset to the area of UWIE, offering a nuanced and potent solution through the integration of multiple branches and feature fusion, validated on the UFO120 dataset. The conclusions underscore the significance of the MB-DNN-FF in advancing the state-of-the-art in UWIE, opening avenues for future research and practical applications in marine sciences, surveillance, and exploration.

A classification-based generative approach to selective targeting of global slow oscillations during sleep.

Given sleep's crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5-1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability. This study targets a specific type of SOs, widespread on the electrode manifold in a short delay ("global SOs"), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile. We introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance. Our study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization. Our research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.

A two-fold deep-learning strategy to correct and downscale winds over mountains

Abstract. Assessing wind fields at a local scale in mountainous terrain has long been a scientific challenge, partly because of the complex interaction between large-scale flows and local topography. Traditionally, the operational applications that require high-resolution wind forcings rely on downscaled outputs of numerical weather prediction systems. Downscaling models either proceed from a function that links large-scale wind fields to local observations (hence including a corrective step) or use operations that account for local-scale processes, through statistics or dynamical simulations and without prior knowledge of large-scale modeling errors. This work presents a strategy to first correct and then downscale the wind fields of the numerical weather prediction model AROME (Application of Research to Operations at Mesoscale) operating at 1300 m grid spacing by using a modular architecture composed of two artificial neural networks and the DEVINE downscaling model. We show that our method is able to first correct the wind direction and speed from the large-scale model (1300 m) and then accurately downscale it to a local scale (30 m) by using the DEVINE downscaling model. The innovative aspect of our method lies in its optimization scheme that accounts for the downscaling step in the computations of the corrections of the coarse-scale wind fields. This modular architecture yields competitive results without suppressing the versatility of the DEVINE downscaling model, which remains unbounded to any wind observations.

3D-DESS MRI with CAIPIRINHA two- and fourfold acceleration for quantitatively assessing knee cartilage morphology.

This study aimed to assess the diagnostic image quality and compare the knee cartilage segmentation results using a controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-accelerated 3D-dual echo steady-state (DESS) research package sequence in the knee. A total of 64 subjects underwent both two- and fourfold CAIPIRINHA-accelerated 3D-DESS and DESS without parallel acceleration technique of the knee on a 3.0T system. Two musculoskeletal radiologists evaluated the images independently for image quality and diagnostic capability following randomization and anonymization. The consistency of automatic segmentation results between sequences was explored using an automatic knee cartilage segmentation research application. The descriptive statistics and inter-observer and inter-method concordance of various acceleration sequences were investigated. P values < .05 were considered significant. For image quality evaluation, the image signal-to-noise ratio and contrast-to-noise ratio decreased with the decrease in scanning time. However, it is accompanied by the reduction of artifacts. Using 3D-DESS without parallel acceleration technique as the standard for cartilage grading diagnosisand the diagnostic agreement of two- and fourfold CAIPIRINHA-accelerated 3D-DESS was good, kappa value was 0.860 (P < .001) and 0.804 (p < 0.001), respectively. Regarding cartilage defects, the sensitivity and specificity of the twofold acceleration 3D-CAIPIRINHA-DESS were 95.56% and 97.70%, and the fourfold CAIPIRINHA-accelerated 3D-DESS were 91.49% and 97.65%, respectively. The intraclass correlation coefficients of various sequences in cartilage segmentation were almost all greater than 0.9. The CAIPIRINHA-accelerated 3D-DESS sequence maintained comparable diagnostic and segmentations performance of knee cartilage after a 60% scan time reduction.

Artificial intelligence for human learning: A review of machine learning techniques used in education research and a suggestion of a learning design model

The goal of this research is to (1) identify the status and development of AI and ML-based learning support systems and their impact on human learning, with a specific focus on techniques employed in previous research, and (2) demonstrate the process of designing a learning support system using AI. Artificial intelligence (AI) and machine learning (ML) technologies have received attention in education. The existing research on AI in education is examined, considering the implications of its application in research. Noteworthy ML techniques from the literature are explained, followed by a discussion on leveraging AI and ML technologies to enhance learning support. Additionally, with consideration of both front-end and back-end approaches,a framework for incorporating AI into education is proposed. Subsequently, a learning design model, Self-regulated Learning with AI Assistants (SLAA), is suggested for addressing the objectives of AI-based learning support system design. The categorization of AI and ML techniques in education research reveals nine types, including supervised learning, mining approaches, and Bayesian techniques. The exploration illustrates how these techniques can be employed in designing a learning support system. This paper provides an empirical overview of AI in education, addresses technological and pedagogical considerations for developing personalized and adaptive learning environments, and outlines the challenges and potential future research directions.

Robust miniature Cas-based transcriptional modulation by engineering Un1Cas12f1 and tethering Sso7d

The miniature V-F CRISPR-Cas12f system has been repurposed for gene editing and transcription modulation. The small size of Cas12f satisfies the packaging capacity of adeno-associated virus (AAV) for gene therapy. However, the efficiency of Cas12f-mediated transcriptional activation varies among different target sites. Here, we developed a robust miniature Cas-based transcriptional activation or silencing system using Un1Cas12f1. We engineered Un1Cas12f1 and the cognate guide RNA and generated miniCRa, which led to a 1319-fold increase in activation of the ASCL1 gene. The activity can be further increased by tethering DNA binding protein Sso7d to miniCRa and generating SminiCRa, which reached 5628-fold activation of the ASCL1 gene and at least hundreds-fold activation at other genes examined. We adopted these mutations of Un1Cas12f1 for transcriptional repression and generated miniCRi or SminiCRi, which led to the repression ∼80% on average of eight genes. We generated an all-in-one AAV vector AIOminiCRi utilized to silence the disease-related gene SERPINA1. AIOminiCRi AAVs led to the 70% repression of the SERPINA1 gene in the Huh-7 cells. In sum, miniCRa, SminiCRa, miniCRi and SminiCRi are robust miniature transcriptional modulators with high specificity, that expand the toolbox for biomedical research and therapeutic applications.

Advances in Research Related to MicroRNA for Diabetic Retinopathy.

Diabetic retinopathy (DR) is a severe microvascular complication of diabetes and is one of the primary causes of blindness in the working-age population in Europe and the United States. At present, no cure is available for DR, but early detection and timely intervention can prevent the rapid progression of the disease. Several treatments for DR are known, primarily ophthalmic treatment based on glycemia, blood pressure, and lipid control, which includes laser photocoagulation, glucocorticoids, vitrectomy, and antivascular endothelial growth factor (anti-VEGF) medications. Despite the clinical efficacy of the aforementioned therapies, none of them can entirely shorten the clinical course of DR or reverse retinopathy. MicroRNAs (miRNAs) are vital regulators of gene expression and participate in cell growth, differentiation, development, and apoptosis. MicroRNAs have been shown to play a significant role in DR, particularly in the molecular mechanisms of inflammation, oxidative stress, and neurodegeneration. The aim of this review is to systematically summarize the signaling pathways and molecular mechanisms of miRNAs involved in the occurrence and development of DR, mainly from the pathogenesis of oxidative stress, inflammation, and neovascularization. Meanwhile, this article also discusses the research progress and application of miRNA-specific therapies for DR.

Towards a taxonomy ofgeodiversity.

Geodiversity is a topical concept in earth and environmental sciences. Geodiversity information is needed to conserve nature, use ecosystem services and achieve sustainable development goals. Despite the increasing demand for geodiversity data, there exists no comprehensive system for categorizing geodiversity. Here, we present a hierarchically structured taxonomy that is potentially applicable in mapping and quantifying geodiversity across different regions, environments and scales. In this taxonomy, the main components of geodiversity are geology, geomorphology, hydrology and pedology. We propose a six-level hierarchical system where the components of geodiversity are classified at progressively lower taxonomic levels based on their genesis, physical-chemical properties and morphology. This comprehensive taxonomy can be used to compile geodiversity information for scientific research and various applications of value to society and nature conservation. Ultimately, this hierarchical system is the first step towards developing a global geodiversity taxonomy. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits.

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits

Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.

A Conceptual Framework for Measuring Organizational Performance through Knowledge Management and Training

This paper proposes a comprehensive conceptual framework designed to evaluate organizational performance by integrating knowledge management (KM) strategies and training initiatives. In contemporary business environments, where knowledge serves as a primary asset, organizations increasingly recognize the critical role of effective knowledge management and training programs in enhancing performance. However, existing frameworks often lack holistic approaches that consider the interplay between these two vital components. Drawing upon theoretical insights from organizational theory, human resource management, and knowledge management literature, this framework synthesizes key dimensions and indicators essential for assessing organizational performance. The proposed framework incorporates elements such as knowledge creation, acquisition, dissemination, utilization, and retention, intertwined with tailored training interventions aimed at enhancing employee competencies and capabilities. By delineating these interconnected dimensions, the framework offers a systematic approach for organizations to evaluate the efficacy of their knowledge management and training initiatives in driving organizational performance. Furthermore, the framework provides a basis for future empirical research and practical applications in diverse organizational contexts, fostering a deeper understanding of the intricate dynamics between knowledge management, training, and organizational performance.

Multifaceted Music Therapy for Depression in Dementia: A Network Meta-Analysis of Randomized Controlled Trials.

This study aimed to assess the efficacy of various music therapy interventions in ameliorating depressive symptoms in dementia patients, utilizing a network meta-analysis approach. We rigorously selected randomized controlled trials focused on music therapy for dementia with depressive symptoms from major electronic databases. The primary outcome measured was the impact on depressive symptoms, with the secondary outcome evaluating dropout rates across different intervention groups and standard care control groups. The research protocol has been duly registered with PROSPERO (Registration ID: CRD42023393059). Our network meta-analysis incorporated 14 randomized controlled trials involving a total of 1080 participants and examined a range of interventions, including active music therapy, listening to music, rhythmic music therapy, singing, and tailored music interventions. The analysis revealed that active music therapy combined with singing emerged as the most effective intervention, demonstrating a significant improvement in depressive symptoms in dementia patients (Standardized Mean Difference [SMD] = -0.89, 95% Confidence Interval [CI]: -1.48 to -0.30). In contrast, listening to music alone showed a smaller effect (SMD = -0.26, 95% CI: -0.71 to 0.20). This study was particularly noteworthy for not showing higher dropout rates compared to standard care, indicating its feasibility and acceptability in clinical settings. The findings of our study indicate that active music therapy combined with singing is an effective approach to reducing depressive symptoms in dementia patients, potentially due to enhanced social interaction. These results offer new perspectives for dementia care, suggesting a promising direction for further research and clinical application.

EDUCATION IN THE ERA OF ARTIFICIAL INTELLIGENCE: AN EVIDENCE FROM DHAKA INTERNATIONAL UNIVERSITY (DIU)

This study investigates the transformative impact of artificial intelligence (AI) on education, assessing both its advantages and drawbacks. Employing the random sampling survey method, data was collected from 160 participants at Dhaka International University. The respondents, predominantly aged between 18-21 years with an average age of 20.6, exhibited varying levels of awareness regarding AI tools- 46 percent were familiar, 44 percent somewhat aware, and 10 percent unfamiliar. Notably, 72.5 percent of participants gained knowledge about artificial intelligence through online sources such as websites, research papers, and forums. The study identified prominent AI tools used by respondents, with ChatGPT being the most widely employed, alongside Google Translator, Microsoft Bard, Grammarly, QuillBot, and YouChat. ChatGPT, in particular, found extensive application in academic studies, research, website development, and app development. Interestingly, 17.5 percent of respondents reported being paid users of AI tools. Beyond usage patterns, the research delved into the efficacy of AI in enhancing education, addressing privacy concerns, and exploring associated challenges, benefits, impacts, ethical considerations, and potential future directions. Employing a Likert scale for assessment, the study concludes by advocating for the integration of AI in education while underscoring the imperative for safeguards to mitigate potential misuse. This comprehensive exploration provides valuable insights into the evolving landscape of AI within the educational sector.

Fourier Ptychographic Microscopy 10 Years on: A Review.

Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.

TALE-based organellar genome editing and gene expression in plants.

TALE-based editors provide an alternative way to engineer the organellar genomes in plants. We update and discuss the most recent developments of TALE-based organellar genome editing in plants. Gene editing tools have been widely used to modify the nuclear genomes of plants for various basic research and biotechnological applications. The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 editing platform is the most commonly used technique because of its ease of use, fast speed, and low cost; however, it encounters difficulty when being delivered to plant organelles for gene editing. In contrast, protein-based editing technologies, such as transcription activator-like effector (TALE)-based tools, could be easily delivered, expressed, and targeted to organelles in plants via Agrobacteria-mediated nuclear transformation. Therefore, TALE-based editors provide an alternative way to engineer the organellar genomes in plants since the conventional chloroplast transformation method encounters technical challenges and is limited to certain species, and the direct transformation of mitochondria in higher plants is not yet possible. In this review, we update and discuss the most recent developments of TALE-based organellar genome editing in plants.

Periosteum Containing Implicit Stem Cells: A Progressive Source of Inspiration for Bone Tissue Regeneration.

The periosteum is known as the thin connective tissue covering most bone surfaces. Its extrusive bone regeneration capacity was confirmed from the very first century-old studies. Recently, pluripotent stem cells in the periosteum with unique physiological properties were unveiled. Existing in dynamic contexts and regulated by complex molecular networks, periosteal stem cells emerge as having strong capabilities of proliferation and multipotential differentiation. Through continuous exploration of studies, we are now starting to acquire more insight into the great potential of the periosteum in bone formation and repair in situ or ectopically. It is undeniable that the periosteum is developing further into a more promising strategy to be harnessed in bone tissue regeneration. Here, we summarized the development and structure of the periosteum, cell markers, and the biological features of periosteal stem cells. Then, we reviewed their pivotal role in bone repair and the underlying molecular regulation. The understanding of periosteum-related cellular and molecular content will help enhance future research efforts and application transformation of the periosteum.

CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation

Multi-sequence magnetic resonance imaging (MRI) has found wide applications in both modern clinical studies and deep learning research. However, in clinical practice, it frequently occurs that one or more of the MRI sequences are missing due to different image acquisition protocols or contrast agent contraindications of patients, limiting the utilization of deep learning models trained on multi-sequence data. One promising approach is to leverage generative models to synthesize the missing sequences, which can serve as a surrogate acquisition. State-of-the-art methods tackling this problem are based on convolutional neural networks (CNN) which usually suffer from spectral biases, resulting in poor reconstruction of high-frequency fine details. In this paper, we propose Conditional Neural fields with Shift modulation (CoNeS), a model that takes voxel coordinates as input and learns a representation of the target images for multi-sequence MRI translation. The proposed model uses a multi-layer perceptron (MLP) instead of a CNN as the decoder for pixel-to-pixel mapping. Hence, each target image is represented as a neural field that is conditioned on the source image via shift modulation with a learned latent code. Experiments on BraTS 2018 and an in-house clinical dataset of vestibular schwannoma patients showed that the proposed method outperformed state-of-the-art methods for multi-sequence MRI translation both visually and quantitatively. Moreover, we conducted spectral analysis, showing that CoNeS was able to overcome the spectral bias issue common in conventional CNN models. To further evaluate the usage of synthesized images in clinical downstream tasks, we tested a segmentation network using the synthesized images at inference. The results showed that CoNeS improved the segmentation performance when some MRI sequences were missing and outperformed other synthesis models. We concluded that neural fields are a promising technique for multi-sequence MRI translation.

Regulation of Photogenerated Redox Species through High Crystallinity Carbon Nitride for Improved C-S Coupling Reactions.

A novel and efficient approach for the synthesis of α, β-unsaturated sulfones through heterogeneous photocatalyzed C-S coupling reactions have been developed. The use of molten-salt method derived carbon nitride (MCN), a transition metal-free polymeric photocatalyst, combined with enhanced crystallinity and potassium iodide as an additive, effectively modulates photogenerated reactive redox species, markedly increasing the overall reaction selectivity. This method achieves the shortest reaction time (2 h) with high yield (up to 95 %) among the reported heterogeneous catalytic C-S bond formation reactions, matching the efficiency of the homogeneous photocatalysts. Furthermore, the application to challenging alkyne substrates has been demonstrated, underscoring the potential for a broad range of applications in pharmaceutical research and synthetic chemistry.