Multimodal Applications Research Articles

NIR Plasmonic Nanozymes: Synergistic Enhancement Mechanism and Multi-Modal Anti-Infection Applications of MXene/MOFs.

Nanozymes are considered as the promising antimicrobial agents due to the enzyme-like activity for chemo-dynamic therapy (CDT). However, it remains a challenge to develop novel nanozyme systems for achieving stimuli-responsive, and efficient nanozyme catalysis with multimodal synergistic enhancement. In this work, a near-infrared (NIR) plasmonic-enhanced nanozyme catalysis and photothermal performance for effective antimicrobial applications are proposed. A Ti3 C2 MXene/Fe-MOFs composite (MXM) with NIR plasmonic-enhanced CDT combined with photothermal properties is successfully developed by loading metal-organic framework (MOF) nanozymes onto Ti3 C2 MXene. The mechanism of NIR induced localized surface plasmon resonance (LSPR)-enhanced CDT and photothermal therapy (PTT)is well explained through activation energy (Ea ), electrochemical impedance spectroscopy (EIS), X-ray photoelectron spectroscopy (XPS), fluorescence analysis experiments, and finite element simulation. It reveals that MXene nanosheets exhibit NIR plasmon exciters and generate hot electrons that can transfer to the surface of Fe-MOFs, promoting the Fenton reaction and enhances CDT. While the photothermal heating of MXene produced by LSPR can also boost the CDT of Fe-MOFs under NIR irradiation. Both in vitro and in vivo experimental results demonstrate that LSPR-induced MXM system has outstanding antimicrobial properties, can promote angiogenesis and collagen deposition, leading to the accelerated wound healing.

Recent Advances in the HPPH-Based Third-Generation Photodynamic Agents in Biomedical Applications.

Photodynamic therapy has emerged as a recognized anti-tumor treatment involving three fundamental elements: photosensitizers, light, and reactive oxygen species. Enhancing the effectiveness of photosensitizers remains the primary avenue for improving the biological therapeutic outcomes of PDT. Through three generations of development, HPPH is a 2-(1-hexyloxyethyl)-2-devinyl derivative of pyropheophorbide-α, representing a second-generation photosensitizer already undergoing clinical trials for various tumors. The evolution toward third-generation photosensitizers based on HPPH involves structural modifications for multimodal applications and the combination of multifunctional compounds, leading to improved imaging localization and superior anti-tumor effects. While research into third-generation HPPH is beneficial for advancing PDT treatment, equal attention should also be directed toward the other two essential elements and personalized diagnosis and treatment methodologies.

Robust multimodal federated learning for incomplete modalities

Consumer electronics are continuously collecting multimodal data, such as audio, video, and so on. A multimodal learning mechanism can be adopted to deal with these data. Due to the consideration of privacy protection, some successful attempts at multimodal federated learning (MMFed) have been conducted. However, real-world multimodal data is usually missing modalities, which can significantly affect the accuracy of the global model in MMFed. Effectively fusing and analyzing multimodal data with incompleteness remains a challenging problem. To tackle this problem, we propose a robust Multimodal Federated Learning Framework for Incomplete Modalities (FedInMM). More specifically, we design a Long Short-Term Memory (LSTM)-based module to extract the information in the temporal sequence. We dynamically learn a weight map to rescale the feature in each modality and formulate the different contributions of features. And then the content of each modality is further fused to form a uniform representation of all modalities of data. By considering the temporal dependency and intra-relation of multi-modalities automatically through the learning stage, this MMFed framework can efficiently mitigate the effects of missing modalities. By using two multimodal datasets, DEAP and AReM, we have conducted comprehensive experiments by simulating different levels of incompleteness. Experimental results demonstrate that FedInMM outperforms other approaches and can train highly accurate models on datasets comprising different incompleteness patterns, which is more appropriate for integration into a practical multimodal application.

Multimodal Use of Contact Endoscopy in Neurosurgery: Case Series with Technical Note and Literature Review

BackgroundOriginally adopted for the cytological screening of cervical and uterine cancer, contact endoscopy (CE) is now widely used in several fields of oncological surgery. The CE method, with magnification power up to 150x, was designed to enhance visualization and identify microscopic changes indicative of pre-cancerous and cancerous lesions at early stages. In this pilot study, we evaluated the multimodal applications of CE during different endoscopic intracranial neurosurgical procedures. MethodsTwenty patients with skull base lesions underwent surgery using different minimally invasive endoscopic approaches (endonasal, transorbital, and supraorbital). CE was used to distinguish the pathology from the surrounding healthy tissue by positioning the endoscope either in proximity or directly onto the target tissue. Special attention was given to the visualization of the margins of the lesion to differentiate compression/displacement from infiltration of the normal surrounding tissue. ResultsWith its unprecedented range of magnification, CE could clearly identify the microvascular pattern and cytological architecture of a tissue not detectable by simple white light endoscopy, with no reported damage due to heat transmission or iatrogenic injuries. All the lesions diagnosed as “presumed neoplastic tissue” by CE were confirmed by histopathology. The most promising results were observed in surgeries for meningioma and pituitary adenoma, as these lesions exhibit distinctive microvascular networks. ConclusionCE represents a new and effective technique for the in vivo identification of pathological microvascular and tissue features, allowing preservation of normal tissue during different endoscopic approaches. The use of CE could improve diagnostic accuracy and assist in intraoperative decision-making, becoming a key tool in various applications in neurosurgical field.

Dual source-powered multifunctional Pt/FePc@Mn-MOF spindle-like Janus nanomotors for active CT imaging-guided synergistic photothermal/chemodynamic therapy

Nanomaterials capable of dual therapeutic effects of chemodynamic therapy (CDT) and photothermal therapy (PTT) is an efficacious strategy in cancer treatment. It is still a challenge to achieve complete apoptosis of tumor tissue in CDT/PTT due to the poor permeability of nanomaterials in tumor tissue. Herein, we prepared a dual-source driven Pt/FePc@Mn-MOF spindle-like Janus nanomotor by a facile oriented connection growth method for computed tomography (CT) imaging-guided CDT and PTT. The high catalase (CAT)-like activity of nanomotors allows the generation of oxygen (O2) bubbles by catalyzing the decomposition of endogenous H2O2, which alleviates the hypoxic state of the tumor microenvironment (TME) and simultaneously drive nanomotors. Pt/FePc@Mn-MOF nanomotor with excellent photothermal conversion efficiency exhibited dual peroxidase (POD)-like and oxidase (OXD)-like activities, which can produce large amounts of ROS to obtain PTT enhanced CDT. Meanwhile, near-infrared light, as “optical brakes”, can trigger Janus nanomotor to realize self-thermophoretic movement. Chemical/NIR-assisted autonomous propulsion can significantly improve the accumulation of Janus nanomotors in solid tumors and enhance their ability to penetrate tumor tissue, thus brings synergistic enhancement effect to PTT and CDT. Moreover, Mn-MOF in nanomotor can deplete the antioxidant GSH by redox reaction to release massive Mn2+, which introduce Mn2+-based CT imaging properties. This novel dual-source controlled Janus nanomotor offers great potential for multimodal therapeutic medical applications.

EVALUATING THE RESEARCH TREND ON THE APPLICATION OF MULTIMODAL APPROACH IN ESL CLASSROOMS

The growing needs for employing multimodality in teaching have sparked research activities all around the world. Due to its broad range of applications, multimodality has received a lot of attention as an approach to be applied in any fields specifically in education. This bibliometric study aimed to evaluate the global research trends in applying multimodality to ESL classrooms based on publication outputs, co-authorships among authors and affiliated countries, and co-occurrences of author keywords. A total of 1303 journal articles published between 1972 and 2022 were retrieved using the Scopus database. The results demonstrate that the number of publications has been steadily increasing since 1972. Approximately 29% of the global total publications were contributed by researchers from the United States and the TESOL Quarterly published the greatest number of articles related to research on the multimodal approach in ESL classrooms. The findings of this review show that the application of multimodality to ESL classrooms may eventually supplant the clusters that dominated multimodality research in the past. In conclusion, the recent progress in implementing the multimodal approach in ESL classrooms includes the following, but not limited to, i) teaching strategies, ii) teaching environments, and iii) teaching and learning with educational technology.

Empowering Language Education: Integrating Translanguaging, Multimodality, and Dialogic Teaching in Chinese Private Universities

Background and aims: This study delves into the dynamic landscape of language education in Chinese private universities, specifically focusing on the application of translanguaging, multimodality, and dialogic teaching to enhance students' literacy development. With English literacy holding paramount importance in the globalized era, junior students in China’s private universities encounter distinct challenges and opportunities. This research investigates the constraints that hinder their literacy competence, examines the efficacy of translanguaging and dialogic pedagogy in bolstering literacy skills, and explores students’ perceptions of their literacy competence and experiences with translanguaging practices. Materials and methods: The study’s significance lies in its potential to shape educational practices and curriculum design within China’s private university context. By harnessing the power of translanguaging, multimodality, and dialogic teaching, this research strives to offer innovative approaches to bolster students’ English literacy proficiency. Results: The findings unravel the intricate interplay between linguistic strategies, instructional techniques, and students’ perceptions, thereby contributing to the creation of effective pedagogical interventions in English literacy education. Conclusion: This research project aims to bridge the gap between theoretical frameworks and practical applications, culminating in actionable insights that resonate with the challenges and aspirations of junior students in Chinese private universities. Through an intricate exploration of language education paradigms, this study ultimately strives to equip students with the linguistic competence and confidence required to thrive in an increasingly interconnected and diverse global society.

Advances in Biocarbon and Soft Material Assembly for Enthalpy Storage: Fundamentals, Mechanisms, and Multimodal Applications.

High-value-added biomass materials like biocarbon are being actively pursued integrating them with soft materials in a broad range of advanced renewable energy technologies owing to their advantages, such as lightweight, relatively low-cost, diverse structural engineering applications, and high energy storage potential. Consequently, the hybrid integration of soft and biomass-derived materials shall store energy to mitigate intermittency issues, primarily through enthalpy storage during phase change. This paper introduces the recent advances in the development of natural biomaterial-derived carbon materials in soft material assembly and its applications in multidirectional renewable energy storage. Various emerging biocarbon materials (biochar, carbon fiber, graphene, nanoporous carbon nanosheets (2D), and carbon aerogel) with intrinsic structures and engineered designs for enhanced enthalpy storage and multimodal applications are discussed. The fundamental design approaches, working mechanisms, and feature applications, such as including thermal management and electromagnetic interference shielding, sensors, flexible electronics and transparent nanopaper, and environmental applications of biocarbon-based soft material composites are highlighted. Furthermore, the challenges and potential opportunities of biocarbon-based composites are identified, and prospects in biomaterial-based soft materials composites are presented.

Defect-management-induced multi-stimulus-responsive mechanoluminescence in Mn2+ doped gallate compound

Exploring mechanoluminescence (ML) materials having multi-stimulus-responsive luminescence is conducive to promoting the development of ML field applications. However, integrating the multi-stimulus-responsive luminescence into a single material is challenging, especially when concerning the unclear multi-stimulus-responsive luminescence mechanisms. Herein, we successfully synthesized a Mn2+-activated broadband multi-stimulus-responsive ML material in a non-centrosymmetric piezoelectric host (LiGaO2: Mn2+). Utilizing the defects’ homology characteristic between ML and persistent/photo-stimulated luminescence, it is confirmed that one can manage the distribution and depth of defects to determine the internal relationship between the multi-stimulus-responsive luminescence and defects. Meanwhile, the V••o and V'Li defects and distribution play a dominant role in multi-mode luminescence. The dynamic process of multi-stimulus-responsive luminescence that charge carriers trapped in deep defects are supplemented to shallow defects can be proved by experimental and DFT calculation results. Based on the excellent multi-stimulus-responsive luminescence performance, the proof-of-concept multi-mode application prospects are demonstrated. This work will deepen our understandings of the multi-stimulus-responsive luminescence mechanism via regulating the distribution and composition of defects to further promote research on flexible X-ray detectors and anti-counterfeiting fields.

Generative Artificial Intelligence and GPT using Deep Learning: A Comprehensive Vision, Applications Trends and Challenges

Generative Artificial intelligence is a prominent and recently emerging subdomain in the field of artificial intelligence. It deals with question-answering based on natural language processing. This paper discusses recent methodologies adopted by researchers in this field. It also discusses GAI and machine learning techniques for multimodal applications like image, text and audio-based data generation. This meta-analysis and survey was done from prominent research up to 2023 from the Scopus Database consisting of reputed and authenticated research papers.The research contribution is twofold 1. To analyze the recent research and applications at the industry level 2. To identify techniques and associated limitations. This would further aid practitioners to address future challenges.

“Green” Fluorescent–Plasmonic Carbon-Based Nanocomposites with Controlled Performance for Mild Laser Hyperthermia

Fluorescent carbon nanodots are a promising nanomaterial for different applications in biophotonics, sensing and optical nanothermometry fields due to their strong fluorescence properties. However, their multi-modal applications are considerably limited, requiring the use of several nanoagents that could solve different tasks simultaneously. In this paper, we report the first experimental results on a facile “green” laser-based synthesis of multi-modal carbon–metallic nanocomposites with tuned optical performance. This simple approach leads to the appearance of finely controlled plasmonic properties in carbon-based nanocomposites whose spectral position is adapted by using an appropriate material. Thus, longer laser ablation provokes 29-fold increase in the absorption intensity of carbon–gold nanocomposites due to the increase in the metal content from 13% (30 s) to 53% (600 s). Despite strong plasmonic properties, the metal presence results in the quenching of the carbon nanostructures’ fluorescence (2.4-fold for C-Au NCs and 3.6-fold for C-Ag NCs for 600 s ablation time). Plasmonic nanocomposites with variable metal content reveal a ~3-fold increase in the laser-to-heat conversion efficiency of carbon nanodots matching the temperature range for mild hyperthermia applications. The findings presented demonstrate a facile approach to expanding the properties of chemically prepared semiconductor nanostructures due to the formation of novel semiconductor–metallic nanocomposites using a “green” approach. Together with the ease in control of their performance, it can considerably increase the impact of semiconductor nanomaterials in various photonic, plasmonic and biomedical applications.

Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging.

T2-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T2W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595-0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568-0.776), although the difference was not statistically significant (p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T2W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model's ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications.

Multimodal Mobile Application to Address Sexual Dysfunction in Hematopoietic Stem Cell Transplant (HCT) Survivors

Background: Sexual dysfunction is the most common complication affecting HCT survivors and is associated with worse patient-reported quality of life (QOL) and psychological distress. Yet, interventions to address sexual dysfunction in HCT survivors are lacking. Methods: We conducted a pilot randomized trial of a multimodal mobile application (SHIFT) to address sexual dysfunction for patients with hematologic malignancies who were at least 3 months post autologous or allogeneic HCT and endorsed sexual dysfunction causing distress. Patients were randomly assigned to SHIFT or enhanced usual care. Intervention participants met with a trained HCT clinician for a brief clinical exam to address biological causes of sexual dysfunction and were then provided access to SHIFT for 8 weeks. SHIFT consists of five modules focused on educating and empowering patients to address their sexual health concerns and addressing the biologic, interpersonal, social, and psychological causes of sexual dysfunction. Patients assigned to enhanced usual care met with the trained HCT clinician for a brief clinical exam and they were not given access to SHIFT. The primary endpoint was feasibility, defined a priori as at least 60% of eligible patients enrolling and 60% of those enrolled completing at least 70% of the SHIFT modules. We assessed patient global satisfaction with sex, interest in sex, orgasm pleasure (PROMIS), QOL (Functional Assessment of Cancer Therapy-Bone Marrow Transplant [FACT-BMT]), and psychological distress (Hospital Anxiety and Depression-Scale [HADS]) at baseline and 8 weeks. To assess the usability of SHIFT, we used the System Usability Scale (>80 indicates excellent usability). We a priori identified a p-value <0.25 as promising for preliminary efficacy in this pilot study. Results: We enrolled 64.2% (61/95) of eligible patients (mean age = 57.2 (SD=14.2), 60% male). In the intervention group, 70.0% completed ≥ 80% of the SHIFT modules, and 66.7% completed all SHIFT modules. Patients assigned to SHIFT used the app for a mean of 155 minutes (Range: 38.1 - 394.9). At 8 weeks, patients randomized to SHIFT reported improved satisfaction with sex (14.6 vs. 12.3, P=0.076), interest in sex (6.7 vs. 5.7, P=0.040), and orgasm pleasure (9.7 vs. 8.2, P=0.106), compared to those assigned to enhanced usual care. SHIFT participants also reported better QOL (115.6 vs. 108.3, P=0.039), anxiety (4.6 vs. 6.4, P=0.043), and depression symptoms (3.6 vs. 5.4, P=0.016) compared to those assigned to enhanced usual care. The SHIFT mean usability score was 80.5 (SD=13.6). Conclusions: A multimodal mobile app to address sexual dysfunction is feasible to use for HCT survivors and has promising preliminary efficacy for improving sexual health outcomes, QOL, and psychological distress. A future multi-site trial is needed to assess the efficacy of SHIFT for improving sexual function and QOL in HCT survivors.

Innovative research on the application of TIRZ theory to multi-mode UAVs

According to the current development of Unmanned Aerial Vehicles (UAVs), it can be known that there are no cases where underwater drones and aerial drones are used in parallel. They are all affected by air and water resistance and are easily disturbed in various environments, making it difficult to move effectively. This research uses TRIZ theory to create a multi-modal unmanned vehicle to innovate and improve the existing situation. The type of equipment produced through this research can effectively move in the air, land, and water. The scope includes TRIZ theory, bionic technology, multi-modal innovative design, research, and application of UAV innovation, and monitoring the improvement of its operational resistance through fluid analysis. The output of this research will effectively promote the development of Multi-mode UAVs and improve the problems of existing UAVs.

Latent morphologies: Encoding architectural features and decoding their structure through artificial intelligence

This article explores the impact of Artificial Intelligence (AI) on the architectural discipline, focusing on generative models and their controllability. While generative models have revolutionized the design process by freeing designers from specific tasks and allowing them to focus on desired results, the reliance on randomness frequently hinders controllability and meaningful experimentation. To address this challenge, the article proposes the construction of an encyclopedic architectural dataset, encompassing various architectural projects and combining images with text for multimodal applications and two methodologies, multi-class StyleGAN and multimodal StyleGAN+CLIP to enhance controllability. Utilizing specific conditions, multi-class StyleGAN enables designers to navigate latent space and identify hidden patterns, while StyleGAN+CLIP integrates text to achieve specific controllability and generate diverse architectural features. Through experimentation, the research showcases the potential of generative models to create structured designs that incorporate existing architectural styles.

Numerical simulation study on microwave joining of Hastelloy C-276 plates using Inconel-718 interface powder

Microwave joining is an emerging technique for the joining of metallic materials because it can join metals faster and offers uniform heating. The current work studies the multi-physics simulation of a microwave-joined Hastelloy C-276 plate with Inconel 718 interface powder inside a multimode microwave applicator at 2.45 GHz and 900 W. The distribution of electric field, temperature, and thermal stress in the joint were analyzed to understand the microwave-joined Hastelloy C-276 plates. The results revealed that the maximum values of electric field strength and temperature at the central plane of symmetry were 7.4 × 104 V/m and 1.43 × 103 ⁰C, respectively. The maximum thermal stress (1.37 × 109N/m2) was estimated using von-Mises criterion. Resistive losses were found to be maximum i.e., 2.72 × 108 W/m3 at the ends of the graphite plate. The study determined the optimum parameters for microwave joining of Hastelloy C-276 plates.

Consumer Perspectives on the Adoption of a Prehabilitation Multimodal Online Program for Patients Undergoing Cancer Surgery.

This study aimed to explore patients' perspectives on the adoption of a prehabilitation multimodal online program. Patients recovering from gastrointestinal cancer surgery at a tertiary hospital between October 2021 and November 2022 were invited to participate. An e-Health program including intensity exercises, nutrition and psychological counselling was used. Patients were instructed to navigate the e-Health program over 24 h using an iPad and then complete the study survey. Patients' characteristics, use of technology, views and minimal expected outcomes from a preoperative online program were collected. Of the 30 patients included, most were female, most reported confidence in the use of technology, most considered the online program safe and most agreed it would be beneficial for their health. "Poor preoperative health" and "lack of motivation and encouragement" were identified as the main barriers to the uptake of a preoperative online program, while program 'simplicity' and perceived 'benefits' were the main facilitators. Significant improvement in postoperative outcomes is perceived to influence patients' willingness to participate in a preoperative multimodal e-Health program. Gastrointestinal cancer patients perceived the adoption of a preoperative multimodal e-Health application as safe to be performed at home and of potential benefit to their health. A range of patient's characteristics, barriers and facilitators to the uptake of an online program were identified. These should be considered in future preoperative multimodal online programs to enhance patient experience, adherence and efficacy. The safety and efficacy of the online prehabilitation program will need to be determined in a larger randomized controlled trial.

Face2Gesture: Translating Facial Expressions Into Robot Movements Through Shared Latent Space Neural Networks

In this work, we present a method for personalizing human-robot interaction by using emotive facial expressions to generate affective robot movements. Movement is an important medium for robots to communicate affective states, but the expertise and time required to craft new robot movements promotes a reliance on fixed preprogrammed behaviors. Enabling robots to respond to multimodal user input with newly generated movements could stave off staleness of interaction and convey a deeper degree of affective understanding than current retrieval-based methods. We use autoencoder neural networks to compress robot movement data and facial expression images into a shared latent embedding space. Then, we use a reconstruction loss to generate movements from these embeddings and triplet loss to align the embeddings by emotion classes rather than data modality. To subjectively evaluate our method, we conducted a user survey and found that generated happy and sad movements could be matched to their source face images. However, angry movements were most often mismatched to sad images. This multimodal data-driven generative method can expand an interactive agent’s behavior library and could be adopted for other multimodal affective applications.

Multimodal Learning With Transformers: A Survey.

Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications and Big Data, Transformer-based multimodal learning has become a hot topic in AI research. This paper presents a comprehensive survey of Transformer techniques oriented at multimodal data. The main contents of this survey include: (1) a background of multimodal learning, Transformer ecosystem, and the multimodal Big Data era, (2) a systematic review of Vanilla Transformer, Vision Transformer, and multimodal Transformers, from a geometrically topological perspective, (3) a review of multimodal Transformer applications, via two important paradigms, i.e., for multimodal pretraining and for specific multimodal tasks, (4) a summary of the common challenges and designs shared by the multimodal Transformer models and applications, and (5) a discussion of open problems and potential research directions for the community.

Design of Water-Soluble Rotaxane-Capped Superparamagnetic, Ultrasmall Fe3O4 Nanoparticles for Targeted NIR Fluorescence Imaging in Combination with Magnetic Resonance Imaging.

Integrating an NIR fluorescent probe with a magnetic resonance imaging (MRI) agent to harvest complementary imaging information is challenging. Here, we have designed water-soluble, biocompatible, noncytotoxic, bright-NIR-emitting, sugar-functionalized, mechanically interlocked molecules (MIMs)-capped superparamagnetic ultrasmall Fe3O4 NPs for targeted multimodal imaging. Dual-functional stoppers containing an unsymmetrical NIR squaraine dye interlocked within a macrocycle to construct multifunctional MIMs are developed with enhanced NIR fluorescence efficiency and durability. One of the stoppers of the axle is composed of a lipophilic cationic TPP+ functionality to target mitochondria, and the other stopper comprises a dopamine-containing catechol group to anchor at the surface of the synthesized Fe3O4 NPs. Fe3O4 NPs surface-coated with targeted NIR rotaxanes help to deliver ultrasmall magnetic NPs specifically inside the mitochondria. Two carbohydrate moieties are conjugated with the macrocycle of the rotaxane via click chemistry to improve the water solubility of MitoSQRot-(Carb-OH)2-DOPA-Fe3O4 NPs. Water-soluble, rotaxane-capped Fe3O4 NPs are used for live-cell mitochondria-targeted NIR fluorescence confocal imaging, 3D and multicolor imaging in combination with T2-weighted MRI on a 9.4 T MR scanner with a high relaxation rate (r2) of 180.7 mM-1 s-1. Biocompatible, noncytotoxic, ultrabright NIR rotaxane-capped superparamagnetic ultrasmall monodisperse Fe3O4 NPs could be a promising agent for targeted multimodal imaging applications.