Thiolated Janus Silsesquioxane Tetrapod: New Precursors for Functional Materials.

Herein, we report synthetic strategies for the development of a bifunctional Janus T4 tetrapod (Janus ring), in which the orthogonal silsesquioxane and organic faces are independently functionalized. An all-cis T4 tetrasilanolate was functionalized to introduce thiol moieties on the silsesquioxane face and naphthyl groups on the organic face to introduce luminescent and self-organization properties. The stepwise synthesis conditions required to prepare such perfectly defined oligomers via a suite of well-defined intermediates and to avoid polymerization or reactions over all eight positions of the tetrapod are explored via 29Si, 13C and 1H NMR, FTIR and TOF-ESI mass spectroscopy. To the best of our knowledge, this is one of the few reports of Janus T4 tetrapods, with different functional groups located on both faces of the molecule, thus expanding the potential range of applications for these versatile precursors.

Large Polyhedral Oligomeric Silsesquioxane Cages: The Isolation of Functionalized POSS with an Unprecedented Si18 O27 Core.

The synthesis of organo-functionalized polyhedral oligomeric silsesquioxanes (POSS, (R-SiO1.5 )n , Tn ) is an area of significant activity. To date, T14 is the largest such cage synthesized and isolated as a single isomer. Herein, we report an unprecedented, single-isomer styryl-functionalized T18 POSS. Unambiguously identified among nine possible isomers by multinuclear solution NMR (1 H, 13 C, and 29 Si), MALDI-MS, FTIR, and computational studies, this is the largest single-isomer functionalized Tn compound isolated to date. A ring-strain model was developed to correlate the 29 Si resonances with the number of 6-, 5-, and/or 4-Si-atom rings that each non-equivalent Si atom is part of. The model successfully predicts the speciation of non-equivalent Si atoms in other families of Tn compounds, demonstrating its general applicability for assigning 29 Si resonances to Si atoms in cage silsesquioxanes and providing a useful tool for predicting Si-atom environments.

Modulating the Electrical Transport in the Two-Dimensional Electron Gas at LaAlO_{3}/SrTiO_{3} Heterostructures by Interfacial Flexoelectricity.

Thin film flexoelectricity is attracting more attention because of its enhanced effect and potential application in electronic devices. Here we find that a mechanical bending induced flexoelectricity significantly modulates the electrical transport properties of the interfacial two-dimensional electron gas (2DEG) at the LaAlO_{3}/SrTiO_{3} (LAO/STO) heterostructure. Under variant bending states, both the carrier density and mobility of the 2DEG are changed according to the flexoelectric polarization direction, showing an electric field effect modulation. By measuring the flexoelectric response of LAO, it is found that the effective flexoelectricity in the LAO thin film is enhanced by 3 orders compared to its bulk. These results broaden the horizon of study on the flexoelectricity effect in the hetero-oxide interface and more research on the oxide interfacial flexoelectricity may be stimulated.

Infrared and Raman spectroscopy of non-conventional hydrogen bonding between N,N'-disubstituted urea and thiourea groups: a combined experimental and theoretical investigation.

The variety of H bond (HB) interactions is a source of inspiration for bottom-up molecular engineering through self-aggregation. Non-conventional intermolecular HBs between N,N'-disubstituted urea and thiourea are studied in detail by vibrational spectroscopies and ab initio calculations. Raman and IR mode assignments are given. We show that it is possible to study selectively the different intermolecular bifurcated intra- and inter-dimer HBs with the two types of HB acceptors. Through the ab initio calculation, the thioamide I mode, a specific marker of N-HS[double bond, length as m-dash]C HB interactions, is unambiguously identified.

Bis-clickable Mesoporous Silica Nanoparticles: Straightforward Preparation of Light-Actuated Nanomachines for Controlled Drug Delivery with Active Targeting.

Bis(clickable) mesoporous silica nanospheres (ca. 100 nm) were obtained by the co-condensation of TEOS with variable amounts (2-5 % each) of two clickable organosilanes in the presence of CTAB. Such nanoparticles could be easily functionalized with two independent functions using the copper-catalyzed alkyne-azide cycloaddition (CuAAC) reaction to transform them into nanomachines bearing cancer cell targeting ligands with the ability to deliver drugs on-demand. The active targeting was made possible after anchoring folic acid by CuAAC click reaction, whereas the controlled delivery was performed by clicked azobenzene fragments. Indeed, the azobenzene groups are able to obstruct the pores of the nanoparticles in the dark whereas upon irradiation in the UV or in the blue range, their trans-to-cis photoisomerization provokes disorder in the pores, enabling the delivery of the cargo molecules. The on-command delivery was proven in solution by dye release experiments, and in vitro by doxorubicin delivery. The added value of the folic acid ligand was clearly evidenced by the difference of cell killing induced by doxorubicin-loaded nanoparticles under blue irradiation, depending on whether the particles featured the clicked folic acid ligand or not.

Evaporation-induced self-structuring of organised silica nanohybrid films through cooperative physical and chemical interactions.

In this work, we develop the concept of evaporation-induced self-structuring as a novel approach for producing organised films by exploiting cooperative physical and chemical interactions under far-from-equilibrium conditions (spin-coating), using sol-gel precursors with multiple functional groups. Thin films of self-structured silsesquioxane nanohybrids have been deposited by spin coating through the sol-gel hydrolysis and condensation of a bridged organosilane bearing self-assembling urea groups. The resulting nanostructure, investigated by FTIR, AFM and SEM, is shown to be highly dependent on the catalyst used (nucleophilic or acidic), and can be further modulated by varying the spinning rate. FTIR studies revealed the presence of highly organised structures under acidic catalysis due to strong hydrogen bonding between urea groups and hydrophobic interactions between long alkylene chains. The preferential orientation of the urea cross-links parallel to the substrate is shown using polarized FTIR experiments.

Enhanced two-photon fluorescence imaging and therapy of cancer cells via Gold@bridged silsesquioxane nanoparticles.

A two-photon photosensitizer with four triethoxysilyl groups is synthesized through the click reaction. This photosensitizer allows the design of bridged silsesquioxane (BS) nanoparticles through a sol-gel process; moreover, gold core BS shells or BS nanoparticles decorated with gold nanospheres are synthesized. An enhancement of the two-photon properties is noted with gold and the nanoparticles are efficient for two-photon imaging and two-photon photodynamic therapy of cancer cells.

Mesoporous-Silica-Functionalized Nanoparticles for Drug Delivery.

The ever-growing interest for finding efficient and reliable methods for treatment of diseases has set a precedent for the design and synthesis of new functional hybrid materials, namely porous nanoparticles, for controlled drug delivery. Mesoporous silica nanoparticles (MSNPs) represent one of the most promising nanocarriers for drug delivery as they possess interesting chemical and physical properties, thermal and mechanical stabilities, and are biocompatibile. In particular, their easily functionalizable surface allows a large number of property modifications further improving their efficiency in this field. This Concept article deals with the advances on the novel methods of functionalizing MSNPs, inside or outside the pores, as well as within the walls, to produce efficient and smart drug carriers for therapy.

A general method for preparing bridged organosilanes with pendant functional groups and functional mesoporous organosilicas.

New organosilica precursors containing two triethoxysilyl groups suitable for the organosilica material formation through the sol-gel process were designed and synthesised. These precursors display alkyne or azide groups for attaching targeted functional groups by copper-catalysed azide-alkyne cycloaddition (CuAAC) and can be used for the preparation of functional organosilicas following two strategies: 1) the functional group is first appended by CuAAC under anhydrous conditions, then the functional material is prepared by the sol-gel process; 2) the precursor is first subjected to the sol-gel process, producing porous, clickable bridged silsesquioxanes or periodic mesoporous organosilicas (PMOs), then the desired functional groups are attached by means of CuAAC. Herein, we show the feasibility of both approaches. A series of bridged bis(triethoxysilane)s with different pending organic moieties was prepared, demonstrating the compatibility of the first approach with many functional groups. In particular, we demonstrate that organic functional molecules bearing only one derivatisation site can be used to produce bridged organosilanes and bridged silsesquioxanes. In the second approach, clickable PMOs and porous bridged silsesquioxanes were prepared from the alkyne- or azide-containing precursors, and thereafter, functionalised with complementary model azide- or alkyne-containing molecules. These results confirmed the potential of this approach as a general methodology for preparing functional organosilicas with high loadings of functional groups. Both approaches give rise to a wide range of new functional organosilica materials.

Hybrid mesoporous silica nanoparticles with pH-operated and complementary H-bonding caps as an autonomous drug-delivery system.

Mesoporous silica nanoparticles (MSNPs) are functionalized with molecular-recognition sites by anchoring a triazine or uracil fragment on the surface. After loading these MSNPs with dyes (propidium iodide or rhodamine B) or with a drug (camptothecin, CPT) they are capped by the complementary fragments, uracil and adenine, respectively, linked to the bulky cyclodextrin ring. These MSNPs are pH-sensitive and indeed, the dye release was observed at acidic pH by continuously monitored fluorescence spectroscopy studies. On the other hand, no dye leakage occurred at neutral pH, hence meeting the non-premature requirement to minimize side effects. In vitro studies were performed and confocal microscopy images demonstrate the internalization of the MSNPs and also dye release in the cells. To investigate the drug-delivery performance, the cytotoxicity of CPT-loaded nanoparticles was tested and cell death was observed. A remarkably lower amount of loaded CPT in the MSNPs (more than 40 times less) proved to be as efficient as free CPT. These results not only demonstrate the drug release after pore opening under lysosomal pH, but they also show the potential use of these MSNPs to significantly decrease the amount of the administered drug.

Objective evaluation of fatigue by EEG spectral analysis in steady-state visual evoked potential-based brain-computer interfaces.

BACKGROUND: The fatigue that users suffer when using steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can cause a number of serious problems such as signal quality degradation and system performance deterioration, users' discomfort and even risk of photosensitive epileptic seizures, posing heavy restrictions on the applications of SSVEP-based BCIs. Towards alleviating the fatigue, a fundamental step is to measure and evaluate it but most existing works adopt self-reported questionnaire methods which are subjective, offline and memory dependent. This paper proposes an objective and real-time approach based on electroencephalography (EEG) spectral analysis to evaluate the fatigue in SSVEP-based BCIs. METHODS: How the EEG indices (amplitudes in δ, θ, α and ß frequency bands), the selected ratio indices (θ/α and (θ + α)/ß), and SSVEP properties (amplitude and signal-to-noise ratio (SNR)) changes with the increasing fatigue level are investigated through two elaborate SSVEP-based BCI experiments, one validates mainly the effectiveness and another considers more practical situations. Meanwhile, a self-reported fatigue questionnaire is used to provide a subjective reference. ANOVA is employed to test the significance of the difference between the alert state and the fatigue state for each index. RESULTS: Consistent results are obtained in two experiments: the significant increases in α and (θ + α)/ß, as well as the decrease in θ/α are found associated with the increasing fatigue level, indicating that EEG spectral analysis can provide robust objective evaluation of the fatigue in SSVEP-based BCIs. Moreover, the results show that the amplitude and SNR of the elicited SSVEP are significantly affected by users' fatigue. CONCLUSIONS: The experiment results demonstrate the feasibility and effectiveness of the proposed method as an objective and real-time evaluation of the fatigue in SSVEP-based BCIs. This method would be helpful in understanding the fatigue problem and optimizing the system design to alleviate the fatigue in SSVEP-based BCIs.

ADFCNN: Attention-Based Dual-Scale Fusion Convolutional Neural Network for Motor Imagery Brain-Computer Interface.

Convolutional neural networks (CNNs) have been successfully applied to motor imagery (MI)-based brain-computer interface (BCI). Nevertheless, single-scale CNN fail to extract abundant information over a wide spectrum from EEG signals, while typical multi-scale CNNs cannot effectively fuse information from different scales with concatenation-based methods. To overcome these challenges, we propose a new scheme equipped with attention-based dual-scale fusion convolutional neural network (ADFCNN), which jointly extracts and fuses EEG spectral and spatial information at different scales. This scheme also provides novel insight through self-attention for effective information fusion from different scales. Specifically, temporal convolutions with two different kernel sizes identify EEG µ and ß rhythms, while spatial convolutions at two different scales generate global and detailed spatial information, respectively, and the self-attention mechanism performs feature fusion based on the internal similarity of the concatenated features extracted by the dual-scale CNN. The proposed scheme achieves the superior performance compared with state-of-the-art methods in subject-specific motor imagery recognition on BCI Competition IV dataset 2a, 2b and OpenBMI dataset, with the cross-session average classification accuracies of 79.39% and significant improvements of 9.14% on BCI-IV2a, 87.81% and 7.66% on BCI-IV2b, 65.26% and 7.2% on OpenBMI dataset, and the within-session average classification accuracies of 86.87% and significant improvements of 10.89% on BCI-IV2a, 87.26% and 8.07% on BCI-IV2b, 84.29% and 5.17% on OpenBMI dataset, respectively. What is more, ablation experiments are conducted to investigate the mechanism and demonstrate the effectiveness of the dual-scale joint temporal-spatial CNN and self-attention modules. Visualization is also used to reveal the learning process and feature distribution of the model.

A designed 5-fluorouracil-based bridged silsesquioxane as an autonomous acid-triggered drug-delivery system.

Two new prodrugs, bearing two and three 5-fluorouracil (5-FU) units, respectively, have been synthesized and were shown to efficiently treat human breast cancer cells. In addition to 5-FU, they were intended to form complexes through H-bonds to an organo-bridged silane prior to hydrolysis-condensation through sol-gel processes to construct acid-responsive bridged silsesquioxanes (BS). Whereas 5-FU itself and the prodrug bearing two 5-FU units completely leached out from the corresponding materials, the prodrug bearing three 5-FU units was successfully maintained in the resulting BS. Solid-state NMR ((29) Si and (13) C) spectroscopy show that the organic fragments of the organo-bridged silane are retained in the hybrid through covalent bonding and the (1) H NMR spectroscopic analysis provides evidence for the hydrogen-bonding interactions between the prodrug bearing three 5-FU units and the triazine-based hybrid matrix. The complex in the BS is not affected under neutral medium and operates under acidic conditions even under pH as high as 5 to deliver the drug as demonstrated by HPLC analysis and confirmed by FTIR and (13) C NMR spectroscopic studies. Such functional BS are promising materials as carriers to avoid the side effects of the anticancer drug 5-FU thanks to a controlled and targeted drug delivery.

Enhancing Detection of Multi-Frequency-Modulated SSVEP Using Phase Difference Constrained Canonical Correlation Analysis.

OBJECTIVE: Multi-frequency-modulated visual stimulation scheme has been shown effective for the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) recently, especially in increasing the visual target number with less stimulus frequencies and mitigating the visual fatigue. However, the existing calibration-free recognition algorithms based on the traditional canonical correlation analysis (CCA) cannot provide the merited performance. APPROACH: To improve the recognition performance, this study proposes a phase difference constrained CCA (pdCCA), which assumes that the multi-frequency-modulated SSVEPs share a common spatial filter over different frequencies and have a specified phase difference. Specifically, during the CCA computation, the phase differences of the spatially filtered SSVEPs are constrained using the temporal concatenation of the sine-cosine reference signals with the pre-defined initial phases. MAIN RESULTS: We evaluate the performance of the proposed pdCCA-based method on three representative multi-frequency-modulated visual stimulation paradigms (i.e., based on the multi-frequency sequential coding, the dual-frequency, and the amplitude modulation). The evaluation results on four SSVEP datasets (Dataset Ia, Ib, II, and III) show that the pdCCA-based method can significantly outperform the current CCA method in terms of recognition accuracy. It improves the accuracy by 22.09% in Dataset Ia, 20.86% in Dataset Ib, 8.61% in Dataset II, and 25.85% in Dataset III. SIGNIFICANCE: The pdCCA-based method, which actively controls the phase difference of the multi-frequency-modulated SSVEPs after spatial filtering, is a new calibration-free method for multi-frequency-modulated SSVEP-based BCIs.

Compact Artificial Neural Network Based on Task Attention for Individual SSVEP Recognition With Less Calibration.

OBJECTIVE: Recently, artificial neural networks (ANNs) have been proven effective and promising for the steady-state visual evoked potential (SSVEP) target recognition. Nevertheless, they usually have lots of trainable parameters and thus require a significant amount of calibration data, which becomes a major obstacle due to the costly EEG collection procedures. This paper aims to design a compact network that can avoid the over-fitting of the ANNs in the individual SSVEP recognition. METHOD: This study integrates the prior knowledge of SSVEP recognition tasks into the attention neural network design. First, benefiting from the high model interpretability of the attention mechanism, the attention layer is applied to convert the operations in conventional spatial filtering algorithms to the ANN structure, which reduces network connections between layers. Then, the SSVEP signal models and the common weights shared across stimuli are adopted to design constraints, which further condenses the trainable parameters. RESULTS: A simulation study on two widely-used datasets demonstrates the proposed compact ANN structure with proposed constraints effectively eliminates redundant parameters. Compared to existing prominent deep neural network (DNN)-based and correlation analysis (CA)-based recognition algorithms, the proposed method reduces the trainable parameters by more than 90% and 80% respectively, and boosts the individual recognition performance by at least 57% and 7% respectively. CONCLUSION: Incorporating the prior knowledge of task into the ANN can make it more effective and efficient. The proposed ANN has a compact structure with less trainable parameters and thus requires less calibration with the prominent individual SSVEP recognition performance.

Stimulus-Stimulus Transfer Based on Time-Frequency-Joint Representation in SSVEP-Based BCIs.

OBJECTIVE: Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) require extensive and costly calibration to achieve high performance. Using transfer learning to re-use existing calibration data from old stimuli is a promising strategy, but finding commonalities in the SSVEP signals across different stimuli remains a challenge. METHOD: This study presents a new perspective, namely time-frequency-joint representation, in which SSVEP signals corresponding to different stimuli can be synchronized, and thus can emphasize common components. According to this time-frequency-joint representation, an adaptive decomposition technique based on the multi-channel adaptive Fourier decomposition (MAFD) is proposed to adaptively decompose SSVEP signals of different stimuli simultaneously. Then, common components can be identified and transferred across stimuli. RESULTS: A simulation study on public SSVEP datasets demonstrates that the proposed stimulus-stimulus transfer method has the ability to extract and transfer these common components across stimuli. By using calibration data from eight source stimuli, the proposed stimulus-stimulus transfer method can generate SSVEP templates of other 32 target stimuli. It boosts the ITR of the stimulus-stimulus transfer based recognition method from 95.966 bits/min to 123.684 bits/min. CONCLUSION: By extracting and transfer common components across stimuli in the proposed time-frequency-joint representation, the proposed stimulus-stimulus transfer method produces good classification performance without requiring calibration data of target stimuli. SIGNIFICANCE: This study provides a synchronization standpoint to analyze and model SSVEP signals. In addition, the proposed stimulus-stimulus method shortens the calibration time and thus improve comfort, which could facilitate real-world applications of SSVEP-based BCIs.

Online Adaptation Boosts SSVEP-Based BCI Performance.

OBJECTIVE: A user-friendly steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) prefers no calibration for its target recognition algorithm, however, the existing calibration-free schemes perform still far behind their calibration-based counterparts. To tackle this issue, learning online from the subject's unlabeled data is investigated as a potential approach to boost the performance of the calibration-free SSVEP-based BCIs. METHODS: An online adaptation scheme is developed to tune the spatial filters using the online unlabeled data from previous trials, and then developing the online adaptive canonical correlation analysis (OACCA) method. RESULTS: A simulation study on two public SSVEP datasets (Dataset I and II) with a total of 105 subjects demonstrated that the proposed online adaptation scheme can boost the CCA's averaged information transfer rate (ITR) from 94.60 to 158.87 bits/min in Dataset I and from 85.80 to 123.91 bits/min in Dataset II. Furthermore, in our online experiment it boosted the CCA's ITR from 55.81 bits/min to 95.73 bits/min. More importantly, this online adaptation scheme can be easily combined with any spatial filtering-based algorithms to achieve online learning. CONCLUSION: By online adaptation, the proposed OACCA performed much better than the calibration-free CCA, and comparable to the calibration-based algorithms. SIGNIFICANCE: This work provides a general way for the SSVEP-based BCIs to learn online from unlabeled data and thus avoid calibration.

Mesoporous organosilicas with thiol functionalised pores: multifunctional dendrimers as sacrificial building block and template.

The synthesis of multifunctional poly(amidoamine) (PAMAM)-based dendrimers containing a cleavable disulfide linker within each arm of the dendrimer, together with condensable triethoxysilyl groups on the periphery of the dendrimer, is described. The dendrimers were mixed with 1,4-bis(triethoxysilyl)benzene and subsequently transformed into silsesquioxane gels or periodic mesoporous organosilicas (PMOs) to generate materials with dendrimers covalently embedded within the interior of the silsesquioxane networks. Subsequent treatment of the gels with dithiothreitol enabled the core of the dendrimers to be selectively cleaved at the disulfide site, thus generating thiol functions localised within the pores. The effect of different dendrimer generations on the reactivity of the pendant thiol functions was probed by impregnation with gold salts, which were reduced to obtain gold nanoparticles within the pore networks of the gels and PMOs. The gels yielded polydisperse gold nanoparticles (2 to 70 nm) with dimensions modulated by the generation of the dendrimer, together with well-defined gold/thiolate clusters with Au⋯S distances of 2.3 Å. Such clusters were also observed in the PMO system, together with monodispersed gold nanoparticles with diameters comparable to that of the organised pores in the PMO. The role of surface functionalisation in controlling the formation of gold clusters and/or nanoparticles is discussed.

Common Spatial Pattern Reformulated for Regularizations in Brain-Computer Interfaces.

Common spatial pattern (CSP) is one of the most successful feature extraction algorithms for brain-computer interfaces (BCIs). It aims to find spatial filters that maximize the projected variance ratio between the covariance matrices of the multichannel electroencephalography (EEG) signals corresponding to two mental tasks, which can be formulated as a generalized eigenvalue problem (GEP). However, it is challenging in principle to impose additional regularization onto the CSP to obtain structural solutions (e.g., sparse CSP) due to the intrinsic nonconvexity and invariance property of GEPs. This article reformulates the CSP as a constrained minimization problem and establishes the equivalence of the reformulated and the original CSPs. An efficient algorithm is proposed to solve this optimization problem by alternately performing singular value decomposition (SVD) and least squares. Under this new formulation, various regularization techniques for linear regression can then be easily implemented to regularize the CSPs for different learning paradigms, such as the sparse CSP, the transfer CSP, and the multisubject CSP. Evaluations on three BCI competition datasets show that the regularized CSP algorithms outperform other baselines, especially for the high-dimensional small training set. The extensive results validate the efficiency and effectiveness of the proposed CSP formulation in different learning contexts.

Tunable high acoustic impedance alumina epoxy composite matching for high frequency ultrasound transducer.

Matching layer is a critical component that determines the performance of piezoelectric ultrasound transducer. For most piezoelectric materials, their acoustic impedances are significantly higher than human tissues and organs, so a tunable matching layer with a high acoustic impedance is required for optimizing the acoustic wave transmission. In this article, a high compression fabrication method is presented, with which the acoustic impedance of alumina-epoxy composite matching layer can be tuned from 6.50 to 9.47 MRayl by controlling the applied compression pressure and ratio of the components. The maximum acoustic impedance 9.47 MRayl can be achieved by compressing a mixture of 80% alumina weight ratio under a 62.4 MPa pressure. This enhancement mainly relies on the increased acoustic longitudinal velocity which enlarged the tolerance of high to ultra-high frequency transducer fabrication using quarter wavelength matching design. Furthermore, the attenuation of the matching layer developed by this method is only -10 dB/mm at 40 MHz. The very high acoustic impedance value and very low attenuation make this matching material superior than all reported matching materials, and therefore, can enhance the performance of the ultrasound transducers, especially for medical imaging applications.