Atomic Ruthenium-Promoted Cadmium Sulfide for Photocatalytic Production of Amino Acids from Biomass Derivatives.

Amino acids are the building blocks of proteins and are widely used as important ingredients for other nitrogen-containing molecules. Here, we report the sustainable production of amino acids from biomass-derived hydroxy acids with high activity under visible-light irradiation and mild conditions, using atomic ruthenium-promoted cadmium sulfide (Ru1/CdS). On a metal basis, the optimized Ru1/CdS exhibits a maximal alanine formation rate of 26.0 molAla ⋅ gRu -1 ⋅ h-1, which is 1.7 times and more than two orders of magnitude higher than that of its nanoparticle counterpart and the conventional thermocatalytic process, respectively. Integrated spectroscopic analysis and density functional theory calculations attribute the high performance of Ru1/CdS to the facilitated charge separation and O-H bond dissociation of the α-hydroxy group, here of lactic acid. The operando nuclear magnetic resonance further infers a unique "double activation" mechanism of both the CH-OH and CH3-CH-OH structures in lactic acid, which significantly accelerates its photocatalytic amination toward alanine.

Universal growth of perovskite thin monocrystals from high solute flux for sensitive self-driven X-ray detection.

Metal-halide perovskite thin monocrystals featuring efficient carrier collection and transport capabilities are well suited for radiation detectors, yet their growth in a generic, well-controlled manner remains challenging. Here, we reveal that mass transfer is one major limiting factor during solution growth of perovskite thin monocrystals. A general approach is developed to overcome synthetic limitation by using a high solute flux system, in which mass diffusion coefficient is improved from 1.7×10-10 to 5.4×10-10 m2 s-1 by suppressing monomer aggregation. The generality of this approach is validated by the synthesis of 29 types of perovskite thin monocrystals at 40-90 °C with the growth velocity up to 27.2 µm min-1. The as-grown perovskite monocrystals deliver a high X-ray sensitivity of 1.74×105 µC Gy-1 cm-2 without applied bias. The findings regarding limited mass transfer and high-flux crystallization are crucial towards advancing the preparation and application of perovskite thin monocrystals.

Ultrahigh Bifunctional Photocatalytic CO2 Reduction and H2 Evolution by Synergistic Interaction of Heteroatomic Pt-Ru Dimerization Sites.

Diatomic-site catalysts (DASCs) inherit the excellent performance of single-atom catalysts (SACs) by utilizing two adjacent atomic metal species to achieve functional complementarity and synergistic effects that improve the carbon dioxide reduction reaction (CO2RR) and H2 evolution reaction (HER) kinetics. Herein, we report a method to further improve the catalytic efficiency of Pt by using Pt and Ru single atoms randomly anchored on a g-C3N4 surface, yielding partial Pt-Ru dimers. The synthesized catalyst exhibits extraordinary photocatalytic activity and stability in both the CO2RR and HER processes. In-depth experimentation, the pH-dependent chemical exchange saturation transfer (CEST) imaging nuclear magnetic resonance (NMR) method, and theoretical analyses reveal that the excellent performance is attributed to orbital coupling between the Pt atoms and the neighboring Ru atoms (mainly dxy and dxz), which decreases the orbital energy levels and weakens the bond strength with intermediates, resulting in improved CO2RR and HER performance. This study successfully applies the pH-dependent CEST imaging NMR method to catalytic reactions, and CO2 adsorption is directly observed using CEST 2D imaging maps. This work presents significant potential for a variety of catalytic reaction applications by systematically designing bimetallic dimers with higher activity and stability.

Carbon nitride nanosheet-supported CuO for efficient photocatalytic CO2 reduction with 100% CO selectivity.

The optimal ratio of reaction solutions resulted in excellent performance and product selectivity of CuO/g-C3N4 composites in the photocatalytic CO2 reduction reaction. A pH-dependent chemical exchange saturation transfer (CEST) imaging nuclear magnetic resonance (NMR) method was used to confirm that CuO modification improves the adsorption capacity of CO2.

Selectively Probing the Magnetic Resonance Signals of γ-Aminobutyric Acid in Human Brains In Vivo.

BACKGROUND: Gamma-aminobutyric acid (GABA) is an inhibitory neurotransmitter in human brains, playing a role in the pathogenesis of various psychiatric disorders. Current methods have some non-neglectable shortcomings and noninvasive and accurate detection of GABA in human brains is long-term challenge. PURPOSE: To develop a pulse sequence capable of selectively detecting and quantifying the 1 H signal of GABA in human brains based on optimal controlled spin singlet order. STUDY TYPE: Prospective. SUBJECTS/PHANTOM: A phantom of GABA (pH = 7.3 ± 0.1) and 11 healthy subjects (5 females and 6 males, body mass index: 21 ± 3 kg/m2 , age: 25 ± 4 years). FIELD STRENGTH/SEQUENCE: 7 Tesla, 3 Tesla, GABA-targeted magnetic resonance spectroscopy (GABA-MRS-7 T, GABA-MRS-3 T), magnetization prepared two rapid acquisition gradient echoes sequence. ASSESSMENT: By using the developed pulse sequences applied on the phantom and healthy subjects, the signals of GABA were successfully selectively probed. Quantification of the signals yields the concentration of GABA in the dorsal anterior cingulate cortex (dACC) in human brains. STATISTICAL TESTS: Frequency. RESULTS: The 1 H signals of GABA in the phantom and in the human brains of healthy subjects were successfully detected. The concentration of GABA in the dACC of human brains was 3.3 ± 1.5 mM. DATA CONCLUSION: The developed pulse sequences can be used to selectively probe the 1 H MR signals of GABA in human brains in vivo. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY STAGE: 1.

Ferroelectricity and Thermochromism in a 2D Dion-Jacobson Organic-Inorganic Hybrid Perovskite.

2D organic-inorganic hybrid perovskites (OIHPs) have become one of the hottest research topics due to their excellent environmental stability and unique optoelectronic properties. Recently, the ferroelectricity and thermochromism of 2D OIHPs have attracted increasing interests. Integrating ferroelectricity and thermochromism into perovskites can significantly promote the development of multichannel intelligent devices. Here, a novel 2D Dion-Jacobson OIHP of the formula (3AMP)PbI4 (where 3AMP is 3-(aminomethyl)pyridinium) is reported, which has a remarkable spontaneous polarization value (Ps) of 15.6 µC cm-2 and interesting thermochromism. As far it is known, such a large Ps value is the highest for 2D OIHPs recorded so far. These findings will inspire further exploration and application of multifunctional perovskites.

Probing distribution and dynamics of lithium ions in supermolecule ß-CD-PEO/Li+ solid polymer electrolytes via solid-state NMR.

In this work, the distribution and dynamics of Li+ ions in ß-CD-PEO/Li+ (ß-CD, ß-cyclodextrin; PEO, polyethylene-oxides) crystalline polymer electrolytes were investigated by solid-state NMR to enlighten the ionic conduction mechanism. Specifically, 7Li-6Li REDOR NMR and variable-contact-time 1H-6Li CP/MAS NMR were adopted for the study. The results demonstrate that Li+ ions coordinated by polymer chains have relatively compact spatial density and fast dynamics, which facilitate the improvement of the electrochemical properties. Additionally, the variation of the distribution and dynamics of the Li+ ions and the ionic conduction mechanism were studied and discussed by altering the amount of the Li+ ions. This work deepens our understanding of the distribution and dynamics of Li+ ions in ß-CD-PEO/Li+ crystals and demonstrates possible future applications of solid-state NMR on the study of the polymer electrolytes.

Using optimal controlled singlet spin order to accurately target molecular signal in MRI and MRS.

Magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) have made great successes in clinical diagnosis, medical research, and neurological science. MRI provides high resolution anatomical images of tissues/organs, and MRS provides information of the functional molecules related to a specific tissue/organ. However, it is difficult for classic MRI/MRS to selectively image/probe a specific metabolite molecule other than the water or fat in tissues/organs. This greatly limits their applications on the study of the molecular mechanism(s) of metabolism and disease. Herein, we report a series of molecularly targeted MRI/MRS methods to target specific molecules. The optimal control method was used to efficiently prepare the singlet spin orders of varied multi-spin systems and in turn greatly expand the choice of the targeted molecules in the molecularly targeted MRI/MRS. Several molecules, such as N-acetyl-L-aspartic acid (NAA), dopamine (DA), and a tripeptide (alanine-glycine-glycine, AGG), have been used as targeted molecules for molecularly targeted MRI and MRS. We show in vivo NAA-targeted 1H MRS spectrum of a human brain. The high-resolution signal of NAA suggests a promising way to study important issues in molecular biology at the molecular level, e.g., measuring the local pH value of tissue in vivo, demonstrating the high potential of such methods in medicine.

Distinguishing glutamate and glutamine in in vivo 1 H MRS based on nuclear spin singlet order filtering.

PURPOSE: The signals of glutamate (Glu) and glutamine (Gln) are often significantly overlapped in routine 1 H-MR spectra of human brain in vivo. Selectively probing the signals of Glu and Gln in vivo is very important for the study of the metabolisms in which Glu and Gln are involved. METHODS: The Glu-/Gln- targeted pulse sequences are developed to selectively probe the signals of Glu and Gln. The core part of the Glu-/Gln- targeted pulse sequences lies on the preparation of the nuclear spin singlet orders (SSOs) of the five-spin systems of Glu and Gln. The optimal control method is used to prepare the SSOs of Glu and Gln with high efficiency. RESULTS: The Glu-/Gln- targeted pulse sequences have been applied on phantoms to selectively probe the signals of Glu and Gln. Moreover, in the in vivo experiments, the signals of Glu and Gln in human brains of healthy subjects have been successfully probed separately. CONCLUSION: The developed Glu-/Gln- targeted pulse sequences can be used to distinguish the 1 H-MR signals of Glu and Gln in human brains in vivo. The optimal control method provides an effective way to prepare the SSO of a specific spin system with high efficiency and in turn selectively probe the signals of a targeted molecule.

Performance of Artificial Intelligence-Aided Diagnosis System for Clinically Significant Prostate Cancer with MRI: A Diagnostic Comparison Study.

BACKGROUND: The high level of expertise required for accurate interpretation of prostate MRI. PURPOSE: To develop and test an artificial intelligence (AI) system for diagnosis of clinically significant prostate cancer (CsPC) with MRI. STUDY TYPE: Retrospective. SUBJECTS: One thousand two hundred thirty patients from derivation cohort between Jan 2012 and Oct 2019, and 169 patients from a publicly available data (U-Net: 423 for training/validation and 49 for test and TrumpeNet: 820 for training/validation and 579 for test). FIELD STRENGTH/SEQUENCE: 3.0T/scanners, T2 -weighted imaging (T2 WI), diffusion-weighted imaging, and apparent diffusion coefficient map. ASSESSMENT: Close-loop AI system was trained with an Unet for prostate segmentation and a TrumpetNet for CsPC detection. Performance of AI was tested in 410 internal and 169 external sets against 24 radiologists categorizing into junior, general and subspecialist group. Gleason score >6 was identified as CsPC at pathology. STATISTICAL TESTS: Area under the receiver operating characteristic curve (AUC-ROC); Delong test; Meta-regression I2 analysis. RESULTS: In average, for internal test, AI had lower AUC-ROC than subspecialists (0.85 vs. 0.92, P < 0.05), and was comparable to junior (0.84, P = 0.76) and general group (0.86, P = 0.35). For external test, both AI (0.86) and subspecialist (0.86) had higher AUC than junior (0.80, P < 0.05) and general reader (0.83, P < 0.05). In individual, it revealed moderate diagnostic heterogeneity in 24 readers (Mantel-Haenszel I2  = 56.8%, P < 0.01), and AI outperformed 54.2% (13/24) of readers in summary ROC analysis. In multivariate test, Gleason score, zonal location, PI-RADS score and lesion size significantly impacted the accuracy of AI; while effect of data source, MR device and parameter settings on AI performance is insignificant (P > 0.05). DATA CONCLUSION: Our AI system can match and to some case exceed clinicians for the diagnosis of CsPC with prostate MRI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Direct observation of geometric and sliding ferroelectricity in an amphidynamic crystal.

Sliding ferroelectricity is a recently observed polarity existing in two-dimensional materials. However, due to the weak polarization and poor electrical insulation in these materials, existing experimental evidences are indirect and mostly based on nanoscale transport properties or piezoresponse force microscopy. We report the direct observation of sliding ferroelectricity, using a high-quality amphidynamic single crystal (15-crown-5)Cd3Cl6, which possesses a large bandgap and so allows direct measurement of polarization-electric field hysteresis. This coordination polymer is a van der Waals material, which is composed of inorganic stators and organic rotators as determined by X-ray diffraction and NMR characterization. From density functional theory calculations, we find that after freezing the rotators, an electric dipole is generated in each layer driven by the geometric mechanism, while a comparable ferroelectric polarization originates from the interlayer sliding. The net polarization of these two components can be directly measured and manipulated. Our finding provides insight into low-dimensional ferroelectrics, especially control of the synchronous dynamics of rotating molecules and sliding layers in solids.

The construction of a two-dimensional organic-inorganic hybrid double perovskite ferroelastic with a high T c and narrow band gap.

Two-dimensional (2D) hybrid double perovskites have attracted extensive research interest for their fascinating physical properties, such as ferroelectricity, X-ray detection, light response and so on. In addition, ferroelastics, as an important branch of ferroic materials, exhibits wide prospects in mechanical switches, shape memory and templating electronic nanostructures. Here, we designed a 2D phase-transition double perovskite ferroelastic through a structurally progressive strategy. This evolution is core to our construction process from 0D to 1D and AgBi-based 2D. In this way, we successfully synthesized 2D lead-free ferroelastic (DPA)4AgBiBr8 (DPA = 2,2-dimethylpropan-1-aminium) with a high Curie temperature (T c), which shows a narrower band gap than 0D (DPA)4Bi2Br10 and 1D (DPA)5Pb2Br9. Moreover, the mechanism of structural phase transition and molecular motion are fully characterized by temperature dependent solid-state NMR and single crystal XRD. (DPA)4AgBiBr8 injects power into the discovery of new ferroelastics or the construction and dimensional adjustment in new hybrid double perovskites.

Unexpected Role of Short Chains in Entangled Polymer Networks.

The knowledge of chain entanglement is key to our understanding of the relation between the viscoelastic properties of polymeric material and their microscopic structure and dynamics. This work conducted a detailed study on the role of short chains in the entangled polymer network. A series of poly(ethylene oxide) (PEO) mixtures with bimodal molecular weight distribution were selected for this study. 1H double-quantum (DQ) NMR combined with the rheology measurement was used to investigate the entangled polymer network. We found that short-chain polymers have the potential to significantly alter the entangled polymer network formed by long-chain polymers. Additionally, both the amount of chain ends and the size of the short-chain polymer were found to have clear disentanglement influences on the entangled polymer network. Moreover, adding low molecular weight PEO to the entangle framework formed by the high molecular weight PEO, resulted in the formation of inhomogeneous entangled polymer networks. The effect of low molecular weight polymers on the entangled polymer networks in PEO melts provides a perspective on the molecular level effect of molecular weight distribution (MWD) on entanglement polymer networks.

Multiple-targeting NMR signal selection by optimal control of nuclear spin singlet.

Selectively probing specific molecules in complex mixtures with nuclear magnetic resonance promises new insights into molecular structures or molecular interaction. Such a study often can be further facilitated when two or more objects in chemical moieties of interest can be precisely targeted. Herein, we proposed a novel method to implement the multiple-targeting signal selection by optimal control of the spin singlets of two or more targeted spin systems from one or more molecules. This method can endow the conventional nuclear magnetic resonance (NMR), magnetic resonance image (MRI) and magnetic resonance spectrum (MRS) with the multiple-targeting signal selectivity to selectively probe several targeted molecules and/or chemical groups simultaneously.

Rapid Identification of Adulteration in Edible Vegetable Oils Based on Low-Field Nuclear Magnetic Resonance Relaxation Fingerprints.

Most current approaches applied for the essential identification of adulteration in edible vegetable oils are of limited practical benefit because they require long analysis times, professional training, and costly instrumentation. The present work addresses this issue by developing a novel simple, accurate, and rapid identification approach based on the magnetic resonance relaxation fingerprints obtained from low-field nuclear magnetic resonance spectroscopy measurements of edible vegetable oils. The relaxation fingerprints obtained for six types of edible vegetable oil, including flaxseed oil, olive oil, soybean oil, corn oil, peanut oil, and sunflower oil, are demonstrated to have sufficiently unique characteristics to enable the identification of the individual types of oil in a sample. By using principal component analysis, three characteristic regions in the fingerprints were screened out to create a novel three-dimensional characteristic coordination system for oil discrimination and adulteration identification. Univariate analysis and partial least squares regression were used to successfully quantify the oil adulteration in adulterated binary oil samples, indicating the great potential of the present approach on both identification and quantification of edible oil adulteration.

A-Site Mixing to Adjust the Photovoltaic Performance of a Double-Cation Perovskite: It Is Not Always the Simple Way.

Considerable progress has been made in improving the performance of optoelectronic devices based on hybrid organic-inorganic perovskites of the form ABX3. However, the influences of A-site doping on the structure and dynamics of the inorganic perovskite crystal lattice and, in turn, on the optoelectronic performance of the resulting devices remain poorly understood at an atomic level. This work addresses this issue by combining the results of several experimental characterization methods for three-dimensional MA1-xDMAxPbBr3 perovskite single crystals (MA, methylammonium; DMA, dimethylammonium). The results reveal a two-stage change in lattice with an increase in DMA content, which has completely opposite effects on the optoelectronic performance of the double-cation perovskite. At low DMA concentrations, fast reorientation of incorporated DMA cations strengthens the interaction between MA cations and the lattice without significant lattice distortion, which could suppress lattice fluctuation and thus improve the photovoltaic performance. At high DMA concentrations, the lattice get a severe distortion, leading to poorer photovoltaic performance.

Cooperative Motion in Water-Methanol Clusters Controls the Reaction Rates of Heterogeneous Photocatalytic Reactions.

Detailed information about the influences of the cooperative motion of water and methanol molecules on practical solid-liquid heterogeneous photocatalysis reactions is critical for our understanding of photocatalytic reactions. The present work addresses this issue by applying operando nuclear magnetic resonance (NMR) spectroscopy, in conjunction with density functional theory (DFT) calculations and ab initio molecular dynamics (AIMD) simulations, to investigate the dynamic behaviors of heterogeneous photocatalytic systems with different molar ratios of water to methanol on rutile-TiO2 photocatalyst. The results demonstrate that methanol and water molecules are involved in the cooperative motions, and the cooperation often takes the form of methanol-water clusters that govern the number of methanol molecules reaching to the active sites of the photocatalyst per unit time, as confirmed by the diffusion coefficients of the methanol molecule calculated in the binary methanol-water solutions. Nuclear Overhauser effect spectroscopy experiments reveal that the clusters are formed by the hydrogen bonding between the -OH groups of CH3OH and H2O. The formation of such methanol-water clusters is likely from an energetic standpoint in low-concentration methanol, which eventually determines the yields of methanol reforming products.

Dynamic heterogeneity in homogeneous polymer melts.

Chain entanglement behaviors were studied by 1H Hahn echo nuclear magnetic resonance (NMR) and 1H double-quantum (DQ) NMR experiments. Poly(ethylene oxide) (PEO) was chosen to investigate the chain entanglement behaviors. The 1H Hahn echo NMR results demonstrate that the critical molecular weight of PEO is approximately 6 kg mol-1. Above this critical molecular weight, chain entanglements start to occur in the melts resulting in anisotropic motions of polymer chain. The 1H DQ NMR observations establish that PEO melts with molecular weights above the critical value exhibit dynamical entanglements. The entangled networks, formed by PEO with a molecular weight of 480 kg mol-1 (PEO480), present slow mobility and rather homogeneously distributed chain entanglements, while the entangled networks, formed by PEO with a molecular weight of 255 kg mol-1 (PEO255), present fast mobility and obvious dynamic heterogeneity in the distribution of chain entanglement. Short chain PEOs like that with a molecular weight of 2 kg mol-1 are demonstrated to function like solvents when being added in an appropriate concentration to PEO480, and the dilution effect increases the chain mobility of PEO480. Moreover, properly diluted PEO480 networks exhibit dynamic heterogeneity similar to that observed in PEO255.

FeAture Explorer (FAE): A tool for developing and comparing radiomics models.

In radiomics studies, researchers usually need to develop a supervised machine learning model to map image features onto the clinical conclusion. A classical machine learning pipeline consists of several steps, including normalization, feature selection, and classification. It is often tedious to find an optimal pipeline with appropriate combinations. We designed an open-source software package named FeAture Explorer (FAE). It was programmed with Python and used NumPy, pandas, and scikit-learning modules. FAE can be used to extract image features, preprocess the feature matrix, develop different models automatically, and evaluate them with common clinical statistics. FAE features a user-friendly graphical user interface that can be used by radiologists and researchers to build many different pipelines, and to compare their results visually. To prove the effectiveness of FAE, we developed a candidate model to classify the clinical-significant prostate cancer (CS PCa) and non-CS PCa using the PROSTATEx dataset. We used FAE to try out different combinations of feature selectors and classifiers, compare the area under the receiver operating characteristic curve of different models on the validation dataset, and evaluate the model using independent test data. The final model with the analysis of variance as the feature selector and linear discriminate analysis as the classifier was selected and evaluated conveniently by FAE. The area under the receiver operating characteristic curve on the training, validation, and test dataset achieved results of 0.838, 0.814, and 0.824, respectively. FAE allows researchers to build radiomics models and evaluate them using an independent testing dataset. It also provides easy model comparison and result visualization. We believe FAE can be a convenient tool for radiomics studies and other medical studies involving supervised machine learning.

Solvent Water Controls Photocatalytic Methanol Reforming.

Understanding the role of different solvent molecules for practical solid-liquid heterogeneous photocatalytic reactions is critical for determining the pathway of the reaction. In this study, the operando nuclear magnetic resonance (NMR) method, combined with density functional theory (DFT) calculations, was employed to evaluate the control effect of solvent water in the photocatalytic reforming mechanism of methanol with a Pt-TiO2 catalyst. Results indicate that the presence of water effectively promotes the formation of the HCHO intermediate but inhibits the H2 evolution originating from the switch of the hydrogen source of the H2 formation from CH3OH to H2O. More interestingly, as detected directly in the ab initio molecular dynamics simulation, a small amount of H2O can dissociate, and the evolved -OH species at Ti5c site can greatly reduce the C-H activation barrier of -CH3O, contributing to the formation of oxidation products (e.g., HOCH2OH and CH3OCH2OH) on the Pt-TiO2 surface.