The Growth Mechanism of a Conductive MOF Thin Film in Spray-based Layer-by-layer Liquid Phase Epitaxy.

The layer-by-layer liquid-phase epitaxy (LBL-LPE) method is widely used in preparing metal-organic framework (MOF) thin films with the merits of controlling thickness and out-of-plane orientation for superior performances in applications. The LBL-LPE growth mechanism related to the grain boundary, structure defect, and orientation is critical but very challenging to study. In this work, a novel "in-plane self-limiting and self-repairing" thin-film growth mechanism is demonstrated by the combination study of the grain boundary, structure defect, and orientation of Cu3 (HHTP)2 -xC thin film via microscopic analysis techniques and electrical measurements. This mechanism results a desired high-quality MOF thin film with preferred in-plane orientations at its bottom for the first time and is very helpful for optimizing the LBL-LPE method, understanding the growth cycle-dependent properties of MOF thin film, and inspiring the investigations of the biomimetic self-repairing materials.

A Dual-Ligand Porous Coordination Polymer Chemiresistor with Modulated Conductivity and Porosity.

Single-ligand-based electronically conductive porous coordination polymers/metal-organic frameworks (EC-PCPs/MOFs) fail to meet the requirements of numerous electronic applications owing to their limited tunability in terms of both conductivity and topology. In this study, a new 2D π-conjugated EC-MOF containing copper units with mixed trigonal ligands was developed: Cu3 (HHTP)(THQ) (HHTP=2,3,6,7,10,11-hexahydrotriphenylene, THQ=tetrahydroxy-1,4-quinone). The modulated conductivity (σ≈2.53×10-5  S cm-1 with an activation energy of 0.30 eV) and high porosity (ca. 441.2 m2 g-1 ) of the Cu3 (HHTP)(THQ) semiconductive nanowires provided an appropriate resistance baseline and highly accessible areas for the development of an excellent chemiresistive gas sensor.

Van der Waals Heterostructured MOF-on-MOF Thin Films: Cascading Functionality to Realize Advanced Chemiresistive Sensing.

Heterostructured metal-organic framework (MOF)-on-MOF thin films have the potential to cascade the various properties of different MOF layers in a sequence to produce functions that cannot be achieved by single MOF layers. An integration method that relies on van der Waals interactions, and which overcomes the lattice-matching limits of reported methods, has been developed. The method deposits molecular sieving Cu-TCPP (TCPP=5,10,15,20-tetrakis(4-carboxyphenyl)porphyrin) layers onto semiconductive Cu-HHTP (HHTP=2,3,6,7,10,11-hexahydrotriphenylene) layers to obtain highly oriented MOF-on-MOF thin films. For the first time, the properties in different MOF layers were cascaded in sequence to synergistically produce an enhanced device function. Cu-TCPP-on-Cu-HHTP demonstrated excellent selectivity and the highest response to benzene of the reported recoverable chemiresistive sensing materials that are active at room temperature. This method allows integration of MOFs with cascading properties into advanced functional materials.

Radiomics and dosiomics for predicting radiation-induced hypothyroidism and guiding intensity-modulated radiotherapy.

To guide individualized intensity-modulated radiotherapy (IMRT), we developed and prospectively validated a multiview radiomics risk model for predicting radiation-induced hypothyroidism in patients with nasopharyngeal carcinoma. And simulated radiotherapy plans with same dose-volume-histogram (DVH) but different dose distributions were redesigned to explore the clinical application of the multiview radiomics risk model. The radiomics and dosiomics were built based on selected radiomics and dosiomics features from planning computed tomography and dose distribution, respectively. The multiview radiomics risk model that integrated radiomics, dosiomics, DVH parameters, and clinical factors had better performance than traditional normal tissue complication probability models. And multiview radiomics risk model could identify differences of patient hypothyroidism-free survival that cannot be stratified by traditional models. Besides, two redesigned simulated plans further verified the clinical application and advantage of the multiview radiomics risk model. The multiview radiomics risk model was a promising method to predict radiation-induced hypothyroidism and guide individualized IMRT.

[Prediction of three-dimensional dose distribution in intensity-modulated radiation therapy based on neural network learning].

OBJECTIVE: To establish the association between the geometric anatomical characteristics of the patients and the corresponding three-dimensional (3D) dose distribution of radiotherapy plan via feed-forward back-propagation neural network for clinical prediction of the plan dosimetric features. METHODS: A total of 25 fixed 13-field clinical prostate cancer intensity-modulated radiation therapy (IMRT)/stereotactic body radiation therapy (SBRT) plans were collected with a prescribed dose of 50 Gy. With the distance from each voxel to the planned target volume (PTV) boundary, the distance from each voxel to each organ-at-risk (OAR), and the volume of PTV as the geometric anatomical characteristics of the patients, the voxel deposition dose was used as the plan dosimetric feature. A neural network was used to construct the correlation model between the selected input features and output dose distribution, and the model was trained with 20 randomly selected cases and verified in 5 cases. RESULTS: The constructed model showed a small model training error, small dose differences among the verification samples, and produced accurate prediction results. In the model training, the point-to-point mean dose difference (hereinafter dose difference) of the 3D dose distribution was no greater than 0.0919∓3.6726 Gy, and the average of the relative volume values corresponding to the fixed dose sequence in the DVH (hereinafter DVH difference) did not exceed 1.7%. The dose differences among the 5 samples for validation was 0.1634∓10.5246 Gy with percent dose differences within 2.5% and DVH differences within 3%. The 3D dose distribution showed that the dose difference was small with reasonable predicted dose distribution. This model showed better performances for dose distribution prediction for bladder and rectum than for the femoral heads. CONCLUSION: We established the relationships between the geometric anatomical characteristics of the patients and the corresponding planning 3D dose distribution via feed-forward back-propagation neural network in patients receiving IMRT/SBRT for the same tumor site. The proposed model provides individualized quality standards for automatic plan quality control.