DataSheet1_Partial deligandation activated ZIF-67 for efficient electrocatalytic oxygen reduction reaction.doc

Removing the blocked molecular groups and fully exposing the intrinsic active sites of metal-organic frameworks (MOFs) could give full play to their advantages of multi-active sites and multi-channel mass transfer, which will benefit the electrocatalytic oxygen reduction reaction (ORR) in fuel cells. Here, the partial diligandation-activated ZIF-67 (named as ZIF-67–400) with excellent ORR performance was obtained by simple low-temperature pyrolysis. The ORR electrocatalytic activity exhibits a half-wave potential of 0.82 V and the stability of maintaining 96% activity after 10 h of operation, which is comparable to commercial Pt/C. Further research studies reveal that the morphology, special dodecahedron configuration, and crystal structure of ZIF-67-400 are maintained well during the pyrolysis, but some hydrocarbon groups in the ligands are eliminated, resulting in the active sites being exposed and coordinated with the intrinsic porosity, improving the catalytic performance. This work may provide an alternative path for activating the electrocatalytic performance of metal-organic frameworks by low-temperature annealing.</p

Algorithm framework of this paper.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient’s condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.</div

Fig 2 -

BraTS brain tumor datasets (a) T1, (b) T2, (c) T1ce, (d) Flair, and (e) GT.</p

Comparison of Dice evaluation indexes between different literature methods.

Comparison of Dice evaluation indexes between different literature methods.</p

Dense block structure.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient’s condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.</div

Fig 7 -

Segmentation results of the second group (a) T1 (b) T2 (c) T1ce (d) Flair (e) GT (f) The resulting diagram of the algorithm in this paper.</p

Structure diagram of a transition layer.

A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient’s condition, which has important application significance in clinical medicine. This paper proposes a network model based on the combination of U-net and DenseNet to solve the problems of class imbalance in multi-modal brain tumor image segmentation and the loss of effective information features caused by the integration of features in the traditional U-net network. The standard convolution blocks of the coding path and decoding path on the original network are improved to dense blocks, which enhances the transmission of features. The mixed loss function composed of the Binary Cross Entropy Loss function and the Tversky coefficient is used to replace the original single cross-entropy loss, which restrains the influence of irrelevant features on segmentation accuracy. Compared with U-Net, U-Net++, and PA-Net the algorithm in this paper has significantly improved the segmentation accuracy, reaching 0.846, 0.861, and 0.782 respectively in the Dice coefficient index of WT, TC, and ET. The PPV coefficient index has reached 0.849, 0.883, and 0.786 respectively. Compared with the traditional U-net network, the Dice coefficient index of the proposed algorithm exceeds 0.8%, 4.0%, and 1.4%, respectively, and the PPV coefficient index in the tumor core area and tumor enhancement area increases by 3% and 1.2% respectively. The proposed algorithm has the best performance in tumor core area segmentation, and its Sensitivity index has reached 0.924, which has good research significance and application value.</div

Fig 10 -

Curve changes of loss and iou of the training set and verification set (a) loss change curve (b) iou change curve.</p

Fig 6 -

Segmentation results of the first group (a) T1 (b) T2 (c) T1ce (d) Flair (e) GT (f) The resulting diagram of the algorithm in this paper.</p

Fig 8 -

Segmentation results of the third group(a) T1 (b) T2 (c) T1ce (d) Flair (e) GT (f) The resulting diagram of the algorithm in this paper.</p