Search

Peroxymonosulfate (PMS) driven by halloysite nanotubes (HNTs) modified with nanomanganese cobaltate (MnCo2O4) generates reactive oxygen species (ROS) that offer high degradation efficiency and mineralization rates for many typical antibiotic pollutants, such as ornidazole (ONZ). The experimental results show that halloysite nanotubes (HNTs) modified with nanomanganese cobaltate (MnCo2O4@HNTs denoted as MCO@HNTs) can degrade ONZ completely over a wide pH range (6.08–11.00) with little influence of the pH value. MCO@HNTs + PMS exhibited higher catalytic activity and lower Co- and Mn-ion leaching rates. It also showed a strong anti-interference effect on natural lake water and anions. Additionally, PMS can be quickly activated and consumed in natural lakes to avoid secondary pollution. The roasting of MCO@HNTs showed good catalytic activity and stability after degrading ONZ. The combination of ion quenching and electron paramagnetic resonance (EPR) analysis illustrated that the MCO@HNTs + PMS system had a strong oxidation capacity, and the produced singlet oxygen (1O2) was the main ROS for ONZ degradation. The degradation pathway of ONZ via the MCO@HNTs + PMS system was proposed based on the types of intermediates determined via liquid chromatography-mass spectrometry (LC-MS). This comprehensive study shows the preparation of a simple, environmentally friendly, and cheap PMS activation catalyst that has practical application value in the treatment of antibiotic wastewater and provides a focus on actual water testing with residual amount of PMS.

Peroxymonosulfate (PMS) driven by halloysite nanotubes (HNTs) modified with nanomanganese cobaltate (MnCo2O4) generates reactive oxygen species (ROS) that offer high degradation efficiency and mineralization rates for many typical antibiotic pollutants, such as ornidazole (ONZ). The experimental results show that halloysite nanotubes (HNTs) modified with nanomanganese cobaltate (MnCo2O4@HNTs denoted as MCO@HNTs) can degrade ONZ completely over a wide pH range (6.08–11.00) with little influence of the pH value. MCO@HNTs + PMS exhibited higher catalytic activity and lower Co- and Mn-ion leaching rates. It also showed a strong anti-interference effect on natural lake water and anions. Additionally, PMS can be quickly activated and consumed in natural lakes to avoid secondary pollution. The roasting of MCO@HNTs showed good catalytic activity and stability after degrading ONZ. The combination of ion quenching and electron paramagnetic resonance (EPR) analysis illustrated that the MCO@HNTs + PMS system had a strong oxidation capacity, and the produced singlet oxygen (1O2) was the main ROS for ONZ degradation. The degradation pathway of ONZ via the MCO@HNTs + PMS system was proposed based on the types of intermediates determined via liquid chromatography-mass spectrometry (LC-MS). This comprehensive study shows the preparation of a simple, environmentally friendly, and cheap PMS activation catalyst that has practical application value in the treatment of antibiotic wastewater and provides a focus on actual water testing with residual amount of PMS.

Peroxymonosulfate (PMS) driven by halloysite nanotubes (HNTs) modified with nanomanganese cobaltate (MnCo2O4) generates reactive oxygen species (ROS) that offer high degradation efficiency and mineralization rates for many typical antibiotic pollutants, such as ornidazole (ONZ). The experimental results show that halloysite nanotubes (HNTs) modified with nanomanganese cobaltate (MnCo2O4@HNTs denoted as MCO@HNTs) can degrade ONZ completely over a wide pH range (6.08–11.00) with little influence of the pH value. MCO@HNTs + PMS exhibited higher catalytic activity and lower Co- and Mn-ion leaching rates. It also showed a strong anti-interference effect on natural lake water and anions. Additionally, PMS can be quickly activated and consumed in natural lakes to avoid secondary pollution. The roasting of MCO@HNTs showed good catalytic activity and stability after degrading ONZ. The combination of ion quenching and electron paramagnetic resonance (EPR) analysis illustrated that the MCO@HNTs + PMS system had a strong oxidation capacity, and the produced singlet oxygen (1O2) was the main ROS for ONZ degradation. The degradation pathway of ONZ via the MCO@HNTs + PMS system was proposed based on the types of intermediates determined via liquid chromatography-mass spectrometry (LC-MS). This comprehensive study shows the preparation of a simple, environmentally friendly, and cheap PMS activation catalyst that has practical application value in the treatment of antibiotic wastewater and provides a focus on actual water testing with residual amount of PMS.

Peroxymonosulfate (PMS) driven by halloysite nanotubes (HNTs) modified with nanomanganese cobaltate (MnCo2O4) generates reactive oxygen species (ROS) that offer high degradation efficiency and mineralization rates for many typical antibiotic pollutants, such as ornidazole (ONZ). The experimental results show that halloysite nanotubes (HNTs) modified with nanomanganese cobaltate (MnCo2O4@HNTs denoted as MCO@HNTs) can degrade ONZ completely over a wide pH range (6.08–11.00) with little influence of the pH value. MCO@HNTs + PMS exhibited higher catalytic activity and lower Co- and Mn-ion leaching rates. It also showed a strong anti-interference effect on natural lake water and anions. Additionally, PMS can be quickly activated and consumed in natural lakes to avoid secondary pollution. The roasting of MCO@HNTs showed good catalytic activity and stability after degrading ONZ. The combination of ion quenching and electron paramagnetic resonance (EPR) analysis illustrated that the MCO@HNTs + PMS system had a strong oxidation capacity, and the produced singlet oxygen (1O2) was the main ROS for ONZ degradation. The degradation pathway of ONZ via the MCO@HNTs + PMS system was proposed based on the types of intermediates determined via liquid chromatography-mass spectrometry (LC-MS). This comprehensive study shows the preparation of a simple, environmentally friendly, and cheap PMS activation catalyst that has practical application value in the treatment of antibiotic wastewater and provides a focus on actual water testing with residual amount of PMS.

Peroxymonosulfate (PMS) driven by halloysite nanotubes (HNTs) modified with nanomanganese cobaltate (MnCo2O4) generates reactive oxygen species (ROS) that offer high degradation efficiency and mineralization rates for many typical antibiotic pollutants, such as ornidazole (ONZ). The experimental results show that halloysite nanotubes (HNTs) modified with nanomanganese cobaltate (MnCo2O4@HNTs denoted as MCO@HNTs) can degrade ONZ completely over a wide pH range (6.08–11.00) with little influence of the pH value. MCO@HNTs + PMS exhibited higher catalytic activity and lower Co- and Mn-ion leaching rates. It also showed a strong anti-interference effect on natural lake water and anions. Additionally, PMS can be quickly activated and consumed in natural lakes to avoid secondary pollution. The roasting of MCO@HNTs showed good catalytic activity and stability after degrading ONZ. The combination of ion quenching and electron paramagnetic resonance (EPR) analysis illustrated that the MCO@HNTs + PMS system had a strong oxidation capacity, and the produced singlet oxygen (1O2) was the main ROS for ONZ degradation. The degradation pathway of ONZ via the MCO@HNTs + PMS system was proposed based on the types of intermediates determined via liquid chromatography-mass spectrometry (LC-MS). This comprehensive study shows the preparation of a simple, environmentally friendly, and cheap PMS activation catalyst that has practical application value in the treatment of antibiotic wastewater and provides a focus on actual water testing with residual amount of PMS.

Solid-state nuclear magnetic resonance (NMR) is an essential tool for the study of biological solids, catalysts, and other functional materials. However, NMR has intrinsically low signal sensitivity and dynamic nuclear polarization (DNP) is one of the most effective approaches to enhance NMR sensitivity. DNP enhances NMR signal sensitivity through transferring the high polarization of electron spins to nuclear spins using microwave (mw) irradiation as a perturbation via different DNP mechanisms. As current DNP efficiency is still far from the theoretical limit (660 for 1H NMR), a major focus in DNP research is to develop methods that can maximize DNP enhancements at conditions germane to solid-state NMR, at high magnetic fields, with fast magic angle spinning (MAS), and under variable temperatures. There are many aspects involved in DNP methods development, including the DNP mechanisms clarification and improvement, instrumentation development, better samples preparation methods, and new data processing techniques.DNP mechanisms development is one of the most important aspects in the DNP methods development area. Current diagnostics of DNP mostly rely on the analysis of the nuclear spin dynamics as a function of mw irradiation parameters which provides incomplete (sometimes even misleading) insights into the mechanism diagnosis process. With the help of continuous wave and pulsed electron paramagnetic resonance (EPR) spectrometers at various fields (from 0.35 to 7 Tesla) as well as quantum mechanical simulations, I have developed and standardized a workflow to diagnose DNP mechanisms and improve their efficiencies. Using the developed workflow, I have improved the existing Cross Effect (CE) DNP efficiency significantly by rationally tuning the the EPR spectral density of mixed broad (TEMPO) and narrow (Trityl)-line radicals, suggesting a novel polarizing agent design of one Trityl tethered to at least two TEMPO moieties. With the help of this novel workflow, a new t

Solid-state nuclear magnetic resonance (NMR) is an essential tool for the study of biological solids, catalysts, and other functional materials. However, NMR has intrinsically low signal sensitivity and dynamic nuclear polarization (DNP) is one of the most effective approaches to enhance NMR sensitivity. DNP enhances NMR signal sensitivity through transferring the high polarization of electron spins to nuclear spins using microwave (mw) irradiation as a perturbation via different DNP mechanisms. As current DNP efficiency is still far from the theoretical limit (660 for 1H NMR), a major focus in DNP research is to develop methods that can maximize DNP enhancements at conditions germane to solid-state NMR, at high magnetic fields, with fast magic angle spinning (MAS), and under variable temperatures. There are many aspects involved in DNP methods development, including the DNP mechanisms clarification and improvement, instrumentation development, better samples preparation methods, and new data processing techniques.DNP mechanisms development is one of the most important aspects in the DNP methods development area. Current diagnostics of DNP mostly rely on the analysis of the nuclear spin dynamics as a function of mw irradiation parameters which provides incomplete (sometimes even misleading) insights into the mechanism diagnosis process. With the help of continuous wave and pulsed electron paramagnetic resonance (EPR) spectrometers at various fields (from 0.35 to 7 Tesla) as well as quantum mechanical simulations, I have developed and standardized a workflow to diagnose DNP mechanisms and improve their efficiencies. Using the developed workflow, I have improved the existing Cross Effect (CE) DNP efficiency significantly by rationally tuning the the EPR spectral density of mixed broad (TEMPO) and narrow (Trityl)-line radicals, suggesting a novel polarizing agent design of one Trityl tethered to at least two TEMPO moieties. With the help of this novel workflow, a new t

A new design principle for a mixed broad (TEMPO) and narrow (Trityl) line radical to boost the dynamic nuclear polarization efficiency is electron spin density matching, suggesting a polarizing agent of one Trityl tethered to at least two TEMPO moieties.

Low-rank matrix estimation plays a central role in various applications across science and engineering. Recently, nonconvex formulations based on matrix factorization are provably solved by simple gradient descent algorithms with strong computational and statistical guarantees. However, when the low-rank matrices are asymmetric, existing approaches rely on adding a regularization term to balance the scale of the two matrix factors which in practice can be removed safely without hurting the performance when initialized via the spectral method. In this paper, we provide a theoretical justification to this for the matrix sensing problem, which aims to recover a low-rank matrix from a small number of linear measurements. As long as the measurement ensemble satisfies the restricted isometry property, gradient descent -- in conjunction with spectral initialization -- converges linearly without the need of explicitly promoting balancedness of the factors; in fact, the factors stay balanced automatically throughout the execution of the algorithm. Our analysis is based on analyzing the evolution of a new distance metric that directly accounts for the ambiguity due to invertible transforms, and might be of independent interest., Comment: To appear on IEEE Trans. on Signal Processing

An in-depth knowledge of the interaction of water with amorphous silica is critical to fundamental studies of interfacial hydration water, as well as to industrial processes such as catalysis, nanofabrication, and chromatography. Silica has a tunable surface comprising hydrophilic silanol groups and moderately hydrophobic siloxane groups that can be interchanged through thermal and chemical treatments. Despite extensive studies of silica surfaces, the influence of surface hydrophilicity and chemical topology on the molecular properties of interfacial water is not well understood. In this work, we controllably altered the surface silanol density, and measured surface water diffusivity using Overhauser dynamic nuclear polarization (ODNP) and complementary silica-silica interaction forces across water using a surface forces apparatus (SFA). The results show that increased silanol density generally leads to slower water diffusivity and stronger silica-silica repulsion at short aqueous separations (less than ∼4 nm). Both techniques show sharp changes in hydration properties at intermediate silanol densities (2.0-2.9 nm-2). Molecular dynamics simulations of model silica-water interfaces corroborate the increase in water diffusivity with silanol density, and furthermore show that even on a smooth and crystalline surface at a fixed silanol density, adjusting the spatial distribution of silanols results in a range of surface water diffusivities spanning ∼10%. We speculate that a critical silanol cluster size or connectivity parameter could explain the sharp transition in our results, and can modulate wettability, colloidal interactions, and surface reactions, and thus is a phenomenon worth further investigation on silica and chemically heterogeneous surfaces.

We consider the problem of recovering low-rank matrices from random rank-one measurements, which spans numerous applications including covariance sketching, phase retrieval, quantum state tomography, and learning shallow polynomial neural networks, among others. Our approach is to directly estimate the low-rank factor by minimizing a nonconvex quadratic loss function via vanilla gradient descent, following a tailored spectral initialization. When the true rank is small, this algorithm is guaranteed to converge to the ground truth (up to global ambiguity) with near-optimal sample complexity and computational complexity. To the best of our knowledge, this is the first guarantee that achieves near-optimality in both metrics. In particular, the key enabler of near-optimal computational guarantees is an implicit regularization phenomenon: without explicit regularization, both spectral initialization and the gradient descent iterates automatically stay within a region incoherent with the measurement vectors. This feature allows one to employ much more aggressive step sizes compared with the ones suggested in prior literature, without the need of sample splitting., Comment: 34 pages

Recent work has demonstrated the effectiveness of gradient descent for directly recovering the factors of low-rank matrices from random linear measurements in a globally convergent manner when initialized properly. However, the performance of existing algorithms is highly sensitive in the presence of outliers that may take arbitrary values. In this paper, we propose a truncated gradient descent algorithm to improve the robustness against outliers, where the truncation is performed to rule out the contributions of samples that deviate significantly from the {\em sample median} of measurement residuals adaptively in each iteration. We demonstrate that, when initialized in a basin of attraction close to the ground truth, the proposed algorithm converges to the ground truth at a linear rate for the Gaussian measurement model with a near-optimal number of measurements, even when a constant fraction of the measurements are arbitrarily corrupted. In addition, we propose a new truncated spectral method that ensures an initialization in the basin of attraction at slightly higher requirements. We finally provide numerical experiments to validate the superior performance of the proposed approach., Comment: 30 pages, 3 figures

We study the problem of estimating a low-rank positive semidefinite (PSD) matrix from a set of rank-one measurements using sensing vectors composed of i.i.d. standard Gaussian entries, which are possibly corrupted by arbitrary outliers. This problem arises from applications such as phase retrieval, covariance sketching, quantum space tomography, and power spectrum estimation. We first propose a convex optimization algorithm that seeks the PSD matrix with the minimum $\ell_1$-norm of the observation residual. The advantage of our algorithm is that it is free of parameters, therefore eliminating the need for tuning parameters and allowing easy implementations. We establish that with high probability, a low-rank PSD matrix can be exactly recovered as soon as the number of measurements is large enough, even when a fraction of the measurements are corrupted by outliers with arbitrary magnitudes. Moreover, the recovery is also stable against bounded noise. With the additional information of an upper bound of the rank of the PSD matrix, we propose another non-convex algorithm based on subgradient descent that demonstrates excellent empirical performance in terms of computational efficiency and accuracy., Comment: 12 pages, 7 figures

We consider simultaneously identifying the membership and locations of point sources that are convolved with different band-limited point spread functions, from the observation of their superpositions. This problem arises in three-dimensional super-resolution single-molecule imaging, neural spike sorting, multi-user channel identification, among other applications. We propose a novel algorithm, based on convex programming, and establish its near-optimal performance guarantee for exact recovery in the noise-free setting by exploiting the spectral sparsity of the point source models as well as the incoherence between point spread functions. Furthermore, robustness of the recovery algorithm in the presence of bounded noise is also established. Numerical examples are provided to demonstrate the effectiveness of the proposed approach., Comment: conference version appeared at ISIT 2015 and SAMPTA 2015. arXiv admin note: text overlap with arXiv:1504.06015

We consider simultaneously identifying the membership and locations of point sources that are convolved with different low-pass point spread functions, from the observation of their superpositions. This problem arises in three-dimensional super-resolution single-molecule imaging, neural spike sorting, multi-user channel identification, among others. We propose a novel algorithm, based on convex programming, and establish its near-optimal performance guarantee for exact recovery by exploiting the sparsity of the point source model as well as incoherence between the point spread functions. Numerical examples are provided to demonstrate the effectiveness of the proposed approach., Comment: ISIT 2015

Compressed Sensing suggests that the required number of samples for reconstructing a signal can be greatly reduced if it is sparse in a known discrete basis, yet many real-world signals are sparse in a continuous dictionary. One example is the spectrally-sparse signal, which is composed of a small number of spectral atoms with arbitrary frequencies on the unit interval. In this paper we study the problem of line spectrum denoising and estimation with an ensemble of spectrally-sparse signals composed of the same set of continuous-valued frequencies from their partial and noisy observations. Two approaches are developed based on atomic norm minimization and structured covariance estimation, both of which can be solved efficiently via semidefinite programming. The first approach aims to estimate and denoise the set of signals from their partial and noisy observations via atomic norm minimization, and recover the frequencies via examining the dual polynomial of the convex program. We characterize the optimality condition of the proposed algorithm and derive the expected convergence rate for denoising, demonstrating the benefit of including multiple measurement vectors. The second approach aims to recover the population covariance matrix from the partially observed sample covariance matrix by motivating its low-rank Toeplitz structure without recovering the signal ensemble. Performance guarantee is derived with a finite number of measurement vectors. The frequencies can be recovered via conventional spectrum estimation methods such as MUSIC from the estimated covariance matrix. Finally, numerical examples are provided to validate the favorable performance of the proposed algorithms, with comparisons against several existing approaches., Comment: 14 pages, 10 figures