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This study explores the effect of suppressed surface charges on the sedimentation behaviour and fabric of kaolinite clay. In particular, sedimentation behaviours were compared between negatively charged natural kaolin clays and surface-treated and hence electrically suppressed kaolin clays by methylene blue adsorption. The results clearly indicate that the negative surface charge impeded the sedimentation rate of kaolin by resisting particle aggregation. Such an effect appeared the most significant when the surrounding pH was less than the isoelectric point (IEP) of the mineral edges. Under a pH less than IEP, the flocculation time and the final void ratio remarkably dropped after the negative charge neutralisation. This was also corroborated by the scanning electron microscopy images, which further revealed the fabric transition from an edge-to-face flocculated structure to a face-to-face aggregated structure due to the absence of interparticle electrical forces. Conversely, there was no significant fabric change at pH higher than or near the IEP, even though the sedimentation behaviours were accelerated by forming larger flocs. This study imparts important insights into the fundamentals of microstructure and resulting sedimentation behaviour of clayey soils and how they can be changed in electrically neutralised clay. 

The MRI-derived brain network serves as a pivotal instrument in elucidating both the structural and functional aspects of the brain, encompassing the ramifications of diseases and developmental processes. However, prevailing methodologies, often focusing on synchronous BOLD signals from functional MRI (fMRI), may not capture directional influences among brain regions and rarely tackle temporal functional dynamics. In this study, we first construct the brain-effective network via the dynamic causal model. Subsequently, we introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE). This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks via an ordinary differential equation (ODE) model, which characterizes spatial-temporal brain dynamics. Our framework is validated on several clinical phenotype prediction tasks using two independent publicly available datasets (HCP and OASIS). The experimental results clearly demonstrate the advantages of our model compared to several state-of-the-art methods. 

Charge transfer is a fundamental interface process that can be harnessed for light detection, photovoltaics, and photosynthesis. Recently, charge transfer was exploited in nanophotonics to alter plasmon polaritons by involving additional non-polaritonic materials to activate the charge transfer. Yet, direct charge transfer between polaritonic materials has not been demonstrated. We report the direct charge transfer in pure polaritonic van der Waals (vdW) heterostructures of α-MoO3/graphene. We extracted the Fermi energy of 0.6 eV for graphene by infrared nano-imaging of charge transfer hyperbolic polaritons in the vdW heterostructure. This unusually high Fermi energy is attributed to the charge transfer between graphene and α-MoO3. Moreover, we have observed charge transfer hyperbolic polaritons in multiple energy–momentum dispersion branches with a wavelength elongation of up to 150%. With the support from the density functional theory calculation, we find that the charge transfer between graphene and α-MoO3, absent in mechanically assembled vdW heterostructures, is attributed to the relatively pristine heterointerface preserved in the epitaxially grown vdW heterostructure. The direct charge transfer and charge transfer hyperbolic polaritons demonstrated in our work hold great promise for developing nano-optical circuits, computational devices, communication systems, and light and energy manipulation devices. 

The modeling of the interaction between brain structure and function using deep learning techniques has yielded remarkable success in identifying potential biomarkers for different clinical phenotypes and brain diseases. However, most existing studies focus on one-way mapping, either projecting brain function to brain structure or inversely. This type of unidirectional mapping approach is limited by the fact that it treats the mapping as a one-way task and neglects the intrinsic unity between these two modalities. Moreover, when dealing with the same biological brain, mapping from structure to function and from function to structure yields dissimilar outcomes, highlighting the likelihood of bias in one-way mapping. To address this issue, we propose a novel bidirectional mapping model, named Bidirectional Mapping with Contrastive Learning (BMCL), to reduce the bias between these two unidirectional mappings via ROI-level contrastive learning. We evaluate our framework on clinical phenotype and neurodegenerative disease predictions using two publicly available datasets (HCP and OASIS). Our results demonstrate the superiority of BMCL compared to several state-of-the-art methods. 

Abstract Element isotopes are characterized by distinct atomic masses and nuclear spins, which can significantly influence material properties. Notably, however, isotopes in natural materials are homogenously distributed in space. Here, we propose a method to configure material properties by repositioning isotopes in engineered van der Waals (vdW) isotopic heterostructures. We showcase the properties of hexagonal boron nitride (hBN) isotopic heterostructures in engineering confined photon-lattice waves—hyperbolic phonon polaritons. By varying the composition, stacking order, and thicknesses of h¹⁰BN and h¹¹BN building blocks, hyperbolic phonon polaritons can be engineered into a variety of energy-momentum dispersions. These confined and tailored polaritons are promising for various nanophotonic and thermal functionalities. Due to the universality and importance of isotopes, our vdW isotope heterostructuring method can be applied to engineer the properties of a broad range of materials. 

This paper examines the learning experiences of undergraduate students who conducted research as part of a multidisciplinary team. The research project involved five undergraduate students with different backgrounds in engineering as well as in arts and sciences, supervised by four architecture and civil engineering faculty and their three PhD students. The research investigates the behavior of new Tessellated Structural-Architectural (TeSA) systems made of repetitive patterns of tiles (tessellations) that are both aesthetically appealing and load bearing. The undergraduate students worked on three tasks: (1) studying the behavior of TeSA shear walls using small scale earthquake simulator tests, (2) studying the shear capacity of reinforced concrete TeSA tiles, and (3) studying the effect of different shapes and interlocking patterns on the performance of small scale TeSA beams. The undergraduate students used hands-on experiments and laboratory testing to study the performance of 3D printed or prefabricated interlocking tessellations. This paper discusses the technical skills, fundamental concepts, and power skills (communicating, writing, presenting, etc.) that the students obtained, as well as the challenges that they encountered. The students found the process of developing and executing hands-on experiments and analyzing experimental results effective for learning new technologies and fundamental concepts. These concepts included 3D printing methods, natural frequency of a structure, and structural response subjected to a shear force. Peer learning, collaboration between students with different backgrounds, and group discussions with all the team members facilitated a deeper understanding and broader perspective on design, performance, and construction of TeSA systems. The project took place during the COVID-19 pandemic, and the students found working and meeting remotely challenging at times. Proper guidance and timely feedback by the project investigators and their PhD students helped with resolving the challenges.