Deliberate Charge Conjugation Symmetry Breaking for π‑Conjugated Electron Acceptor Design

Scientific progress in searching for new π-conjugated electron-accepting materials has been greatly limited to the commonly used fullerene-based structures. In this work, a fundamental link is established between frustrated electron–phonon coupling and broken charge conjugation symmetry for π-conjugated systems. This effectively leads to a practical materials design route of using naturally abundant five-membered and six-membered conjugated rings to systematically construct a library of electron-accepting materials beyond fullerenes. Coupled to the recently developed π-conjugated copolymer electron donors of targeted optical bandgaps, a comprehensive list of electron donor and acceptor combinations with the optimal power conversion efficiencies is proposed for the photovoltaic solar cell applications

Results of different methods for underwater object detection.

Results of different methods for underwater object detection.</p

Effectiveness of each component of our model.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.</div

Different query decoding mechanisms.

(a) Basic, (b) Dense Query Recollection, (c) Learnable Query Recall.</p

Architecture of network’s transformer.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.</div

Effectiveness of the middle dimension.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.</div

Comparison between models on RUOD.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.</div

Architecture of AdaptFFN.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.</div

Analysis of small-scale objects on the DUO dataset.

Analysis of small-scale objects on the DUO dataset.</p

Magnetoresponsive Poly(ether sulfone)-Based Iron Oxide <i>cum</i> Hydrogel Mixed Matrix Composite Membranes for Switchable Molecular Sieving

Stimuli-responsive membranes that can adjust mass transfer and interfacial properties “on demand” have drawn large interest over the last few decades. Here, we designed and prepared a novel magnetoresponsive separation membrane with remote switchable molecular sieving effect by simple one-step and scalable nonsolvent induced phase separation (NIPS) process. Specifically, poly­(ether sulfone) (PES) as matrix for an anisotropic membrane, prefabricated poly­(<i>N</i>-isopropylacrylamide) (PNIPAAm) nanogel (NG) particles as functional gates, and iron oxide magnetic nanoparticles (MNP) as localized heaters were combined in a synergistic way. Before membrane casting, the properties of the building blocks, including swelling property and size distribution for NG, and magnetic property and heating efficiency for MNP, were investigated. Further, to identify optimal film casting conditions for membrane preparation by NIPS, in-depth rheological study of the effects of composition and temperature on blend dope solutions was performed. At last, a composite membrane with 10% MNP and 10% NG blended in a porous PES matrix was obtained, which showed a large, reversible, and stable magneto-responsivity. It had 9 times higher water permeability at the “on” state of alternating magnetic field (AMF) than at the “off”-state. Moreover, the molecular weight cutoff of such membrane could be reversibly shifted from ∼70 to 1750 kDa by switching off or on the external AMF, as demonstrated in dextran ultrafiltration tests. Overall, it has been proved that the molecular sieving performance of the novel mixed matrix composite membrane can be controlled by the swollen/shrunken state of PNIPAAm NG embedded in the nanoporous barrier layer of a PES-based anisotropic porous matrix, via the heat generation of nearby MNP. And the structure of such membrane can be tailored by the NIPS process conditions. Such membrane has potential as enabling material for remote-controlled drug release systems or devices for tunable fractionations of biomacromolecule/-particle mixtures