Parahydrogen is an inexpensive and readily available source of hyperpolarization used to enhance magnetic resonance signals by up to four orders of magnitude above thermal signals obtained at ∼10 T. A significant challenge for applications is fast signal decay after hyperpolarization. Here we use parahydrogen-based polarization transfer catalysis at microtesla fields (first introduced as SABRE-SHEATH) to hyperpolarize C spin pairs and find decay time constants of 12 s for magnetization at 0.3 mT, which are extended to 2 min at that same field, when long-lived singlet states are hyperpolarized instead. Enhancements over thermal at 8.5 T are between 30 and 170 fold (0.02 to 0.12% polarization). We control the spin dynamics of polarization transfer by choice of microtesla field, allowing for deliberate hyperpolarization of either magnetization or long-lived singlet states. Density functional theory calculations and experimental evidence identify two energetically close mechanisms for polarization transfer: First, a model that involves direct binding of the C pair to the polarization transfer catalyst and, second, a model transferring polarization through auxiliary protons in substrates.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580346 | PMC |
http://dx.doi.org/10.1021/acs.jpclett.7b00987 | DOI Listing |
Se Pu
January 2025
Key Laboratory of Radiopharmaceuticals, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
Lipids are indispensable components of living organisms and play pivotal roles in cell-membrane fluidity, energy provision, and neurotransmitter transmission and transport. Lipids can act as potential biomarkers of diseases given their abilities to indicate cell-growth status. For example, the lipid-metabolism processes of cancer cells are distinct from those of normal cells owing to their rapid proliferation and adaptation to ever-changing biological environments.
View Article and Find Full Text PDFJ Phys Chem C Nanomater Interfaces
December 2024
Department of Materials Science, University of Milano-Bicocca, Via Roberto Cozzi 55, 20125 Milano, Italy.
The adsorption of (X = Ni, Pd, and Pt) nanoclusters is simulated by using first-principles methods on MgO(100) and on a MgO monolayer supported on Ag(100), considering the presence of interfacial oxygen. On both the free-standing MgO surface and MgO/Ag, all clusters exhibit robust adhesion and negative charge transfer. molecular dynamics calculations at 200 K demonstrate the stability of the nanoparticles on the MgO/Ag support.
View Article and Find Full Text PDFPhotochem Photobiol Sci
December 2024
Institute of Meteorology and Climate Research Atmospheric Trace Gases and Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe, Germany.
This paper investigates the evolution of changes in surface ultraviolet (UV) radiation globally, emphasizing the significant impacts of key factors influencing its variability, i.e., total column ozone, aerosols, clouds, and surface reflectivity.
View Article and Find Full Text PDFAnal Chem
December 2024
Key Laboratory of Optic-Electric Sensing and Analytical Chemistry for Life Science, MOE, College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology, Qingdao 266042, P. R. China.
The classical electrochemiluminescence (ECL) reagent Ru(bpy) was first doped into CdSe QDs to prepare novel dual-potential color-resolved luminophore Ru-CdSe QDs. Ru-CdSe QDs emitted a strong red ECL signal at a positive potential with coreactant TPrA and a strong green ECL signal at a negative potential with coreactant KSO. As a proof-of-concept application, this work introduced Ru-CdSe QDs into a dual-channel closed bipolar electrode (CBPE) system to construct an ECL biosensor for simultaneous detection of chloramphenicol (CAP) and kanamycin (KAN).
View Article and Find Full Text PDFPolarization and wavelength multiplexed metalenses address the bulkiness of traditional imaging systems. However, despite progress with numerical simulations and parameter scanning, the engineering complexity of classical methods highlights the urgent need for efficient deep learning approaches. This paper introduces a deep learning-driven inverse design model for polarization-multiplexed metalenses, employing propagation phase theory alongside spectral transfer learning to address chromatic dispersion challenges.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!