Dataset Information

Fine-Tuning X-Ray Sensitivity in Organic-Inorganic Hybrids via an Unprecedented Mixed-Ligand Strategy.

ABSTRACT: Crystalline organic-inorganic hybrids, which exhibit colorimetric responses to ionizing radiation, have recently been recognized as promising alternatives to conventional X-ray dosimeters. However, X-ray-responsive organic-inorganic hybrids are scarce and the strategy to fine-tune their detection sensitivity remains elusive. Herein, an unprecedented mixed-ligand strategy is reported to modulate the X-ray detection efficacy of organic-inorganic hybrids. Deliberately blending the stimuli-responsive terpyridine carboxylate ligand (tpc^- ) and the auxiliary pba^- group with different ratios gives rise to two OD thorium-bearing clusters (Th-102 and Th-103) and a 1D coordination polymer (Th-104). Notably, distinct X-ray sensitivity is evident as a function of molar ratio of the tpc^- ligand, following the trend of Th-102 > Th-103 > Th-104. Moreover, Th-102, which is exclusively built from the tpc^- ligands with the highest degree of π-π interactions, exhibits the most sensitive radiochromic and fluorochromic responses toward X-ray with the lowest detection limit of 1.5 mGy. The study anticipates that this mixed-ligand strategy will be a versatile approach to tune the X-ray sensing efficacy of organic-inorganic hybrids.

SUBMITTER: Lu H

PROVIDER: S-EPMC10767407 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Fine-Tuning X-Ray Sensitivity in Organic-Inorganic Hybrids via an Unprecedented Mixed-Ligand Strategy.

Lu Huangjie H Zheng Zhaofa Z Hou Huiliang H Bai Yaoyao Y Qiu Jie J Wang Jian-Qiang JQ Lin Jian J

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20231108 1

Crystalline organic-inorganic hybrids, which exhibit colorimetric responses to ionizing radiation, have recently been recognized as promising alternatives to conventional X-ray dosimeters. However, X-ray-responsive organic-inorganic hybrids are scarce and the strategy to fine-tune their detection sensitivity remains elusive. Herein, an unprecedented mixed-ligand strategy is reported to modulate the X-ray detection efficacy of organic-inorganic hybrids. Deliberately blending the stimuli-responsiv ...[more]

PMID: 37939314

Similar Datasets

Project description:IntroductionFine-tuning (FT) is a generally adopted transfer learning method for deep learning-based magnetic resonance imaging (MRI) reconstruction. In this approach, the reconstruction model is initialized with pre-trained weights derived from a source domain with ample data and subsequently updated with limited data from the target domain. However, the direct full-weight update strategy can pose the risk of "catastrophic forgetting" and overfitting, hindering its effectiveness. The goal of this study is to develop a zero-weight update transfer strategy to preserve pre-trained generic knowledge and reduce overfitting.MethodsBased on the commonality between the source and target domains, we assume a linear transformation relationship of the optimal model weights from the source domain to the target domain. Accordingly, we propose a novel transfer strategy, linear fine-tuning (LFT), which introduces scaling and shifting (SS) factors into the pre-trained model. In contrast to FT, LFT only updates SS factors in the transfer phase, while the pre-trained weights remain fixed.ResultsTo evaluate the proposed LFT, we designed three different transfer scenarios and conducted a comparative analysis of FT, LFT, and other methods at various sampling rates and data volumes. In the transfer scenario between different contrasts, LFT outperforms typical transfer strategies at various sampling rates and considerably reduces artifacts on reconstructed images. In transfer scenarios between different slice directions or anatomical structures, LFT surpasses the FT method, particularly when the target domain contains a decreasing number of training images, with a maximum improvement of up to 2.06 dB (5.89%) in peak signal-to-noise ratio.DiscussionThe LFT strategy shows great potential to address the issues of "catastrophic forgetting" and overfitting in transfer scenarios for MRI reconstruction, while reducing the reliance on the amount of data in the target domain. Linear fine-tuning is expected to shorten the development cycle of reconstruction models for adapting complicated clinical scenarios, thereby enhancing the clinical applicability of deep MRI reconstruction.

Dataset Information

Fine-Tuning X-Ray Sensitivity in Organic-Inorganic Hybrids via an Unprecedented Mixed-Ligand Strategy.

Publications

Fine-Tuning X-Ray Sensitivity in Organic-Inorganic Hybrids via an Unprecedented Mixed-Ligand Strategy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets