NMR investigations of biological and synthetic phosphate-based nanocomposites

The study of complex organic, inorganic and composite systems is greatly facilitated by solid state nuclear magnetic resonance (NMR) spectroscopy. This is especially true for materials lacking crystalline long-range order or having low atomic mass contrast, such as amorphous organic materials, which renders other methods such as x-ray diffraction (XRD) and transmission electron microscopy (TEM) incapable of comprehensive characterization. In this dissertation, a variety of one- and two-dimensional (2D) solid-state NMR measurements are applied to investigate the composition and nanometer-scale structure of a variety of organic-inorganic hybrid systems as well as complex inorganic phases. Bone, which is a natural nanocomposite of an inorganic apatitic phosphate and the organic protein collagen, has been studied by 1H single-resonance, 1H-31P and 1H-13C double-resonance, as well as 1H-13C-31P triple-resonance experiments. Analysis of 31P dephasing by heteronuclear recoupling with dephasing by strong homonuclear interactions of protons (HARDSHIP) has provided information about the size of the apatite nanocrystals. The concentrations of various moieties in the composite, such as the OH-, CO32-,HPO4 2-,H2O-PO43-, and Na in the inorganic apatite, were determined by quantitative spectroscopy via spectral selection of specific chemical moieties. X{lcub} 1H{rcub} HARDSHIP NMR was used to prove their incorporation into the apatite nanocrystals. 31P chemical shift anisotropy (CSA) dephasing experiments as well as 1H{lcub}31P{rcub} rotational echo double resonance (REDOR) experiments have identified and quantified the hydrogen and phosphate species located at the surface and the interior of the apatite crystal. Strongly bound H2O, as well as a layer of viscous water, is present at the organic-inorganic interface, as proven by 1H spin-diffusion detected via 13C and 31P nuclei. Investigation of the proximity of organic moieties to the apatite surface via 13C{lcub}31P{rcub} heteronuclear recoupling experiments provide a structural insight of the organic-inorganic interface.;Biomimetic synthetic organic-inorganic phosphate hybrid materials have been investigated. 31P NMR spectroscopy has enabled identification and quantification of the different types of phosphates in these materials, and the formation of nanocomposites is proven by wideline separation (WISE) NMR with spin diffusion. A bone-replacement material, Si/Zn-doped beta-tricalcium phosphate (TCP), has also been investigated. Spectral selection techniques based on J-modulation and double-quantum filtering have enabled elucidation of the spectrally overlapping silicate Q species. 29Si{lcub} 31P{rcub} REDOR proves that while the silicate is indeed incorporated into the TCP matrix, it is significantly aggregated into ∼7 nm diameter domains. Further, a new class of hybrid systems based on polyamide 6 and phosphate glass was studied, where HARDSHIP has confirmed the formation of nanocomposites of the phosphate glass dispersed in the polyamide matrix. 1H- 31P heteronuclear correlation (HetCor) NMR indicated phosphate-polyamide interactions and alterations of the phosphate glass surface by the polyamide matrix. 13C NMR has also shown that the phosphate glass promotes the crystalline gamma-phase of the polyamide

Discovering gated recurrent neural network architectures

Reinforcement Learning agent networks with memory are a key component in solving POMDP tasks. Gated recurrent networks such as those composed of Long Short-Term Memory (LSTM) nodes have recently been used to improve state of the art in many supervised sequential processing tasks such as speech recognition and machine translation. However, scaling them to deep memory tasks in reinforcement learning domain is challenging because of sparse and deceptive reward function. To address this challenge first, a new secondary optimization objective is introduced that maximizes the information (Info-max) stored in the LSTM network. Results indicate that when combined with neuroevolution, Info-max can discover powerful LSTM-based memory solutions that outperform traditional RNNs. Next, for the supervised learning tasks, neuroevolution techniques are employed to design new LSTM architectures. Such architectural variations include discovering new pathways between the recurrent layers as well as designing new gated recurrent nodes. This dissertation proposes evolution of a tree-based encoding of the gated memory nodes, and shows that it makes it possible to explore new variations more effectively than other methods. The method discovers nodes with multiple recurrent paths and multiple memory cells, which lead to significant improvement in the standard language modeling benchmark task. The dissertation also shows how the search process can be speeded up by training an LSTM network to estimate performance of candidate structures, and by encouraging exploration of novel solutions. Thus, evolutionary design of complex neural network structures promises to improve performance of deep learning architectures beyond human ability to do so.Computer Science

Insight into Bioactivity of In‐situ Trapped Enzyme‐Covalent‐Organic Frameworks

Selecting a suitable support material for enzyme immobilization with excellent biocatalytic activity and stability is a critical aspect in the development of functional biosystems. The highly stable and metal‐free properties of covalent‐organic frameworks (COFs) make them ideal supports for enzyme immobilization. Herein, we constructed three kinds of COFs via a biofriendly and one‐pot synthetic strategy at room temperature in aqueous solution. Among the three developed COFs (COF‐LZU1, RT‐COF‐1 and ACOF‐1), the horseradish peroxidase (HRP)‐incorporated COF‐LZU1 is found to retain the highest activity. Structural analysis reveals that a weakest interaction between the hydrated enzyme and COF‐LZU1, an easiest accessibility by the COF‐LZU1 to the substrate, as well as an optimal conformation of enzyme together promote the bioactivity of HRP‐COF‐LZU1. Furthermore, the COF‐LZU1 is revealed to be a versatile nanoplatform for encapsulating multiple enzymes. The COF‐LZU1 also offers superior protection for the immobilized enzymes under harsh conditions and during recycling. The comprehensive understanding of interfacial interactions of COF host and enzyme guest, the substrate diffusion, as well as the enzyme conformation alteration within COF matrices represents an opportunity to design the ideal biocatalysts and opens a broad range of applications of these nanosystems