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Three-dimensional (3D) printing has exploded in interest as new technologies have opened up a multitude of applications, with stereolithography a particularly successful approach. However, owing to the linear absorption of light, this technique requires photopolymerization to occur at the surface of the printing volume, imparting fundamental limitations on resin choice and shape gamut. One promising way to circumvent this interfacial paradigm is to move beyond linear processes, with many groups using two-photon absorption to print in a truly volumetric fashion. Using two-photon absorption, many groups and companies have been able to create remarkable nanoscale structures, but the laser power required to drive this process has limited print size and speed, preventing widespread application beyond the nanoscale. Here we use triplet fusion upconversion to print volumetrically with less than 4 milliwatt continuous-wave excitation. Upconversion is introduced to the resin by means of encapsulation with a silica shell and solubilizing ligands. We further introduce an excitonic strategy to systematically control the upconversion threshold to support either monovoxel or parallelized printing schemes, printing at power densities several orders of magnitude lower than the power densities required for two-photon-based 3D printing.
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http://dx.doi.org/10.1038/s41586-022-04485-8 | DOI Listing |
J Am Acad Orthop Surg
August 2025
From the Division of Shoulder and Elbow Surgery, Department of Orthopedic Surgery, Indiana University Health, Muncie, IN (Triplet), the Division of Shoulder and Elbow Surgery, Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Sanchez-Sotelo, and Morrey), Division of Orthopedic Oncology,
Substantial bone loss at the time of complex primary and revision shoulder or elbow arthroplasty is challenging. Large bone defects compromise component support and important muscle-tendon units. Megaprosthesis, osteoarticular allografts, vascularized bone transfers, fusions, and allograft prosthetic composites (APCs) have all been described for reconstruction in these difficult situations.
View Article and Find Full Text PDFSci Rep
August 2025
School of Mining Engineering, Heilongjiang University of Science and Technology, Haerbin, 150000, China.
Building segmentation of high-resolution remote sensing images using deep learning effectively reduces labor costs, but still faces the key challenges of effectively modeling cross-scale contextual relationships and preserving fine spatial details. Current Transformer-based approaches demonstrate superior long-range dependency modeling, but still suffer from the problem of progressive information loss during hierarchical feature encoding. Therefore, this study proposed a new semantic segmentation network named SegTDformer to extract buildings in remote sensing images.
View Article and Find Full Text PDFAdv Mater
August 2025
School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi, 16419, Republic of Korea.
Blue phosphorescent organic light-emitting diodes (PhOLEDs) face challenges in achieving high efficiency, color purity, and long device lifetime due to exciton quenching and high energy requirements. In this study, two tetradentate Pt(II) complexes, Pt-impy and Pt-Me-impy, are designed and synthesized by incorporating pyridocarbene in their ligands. Pyridocarbene enhances the electrochemical stability, strengthens triplet metal-to-ligand charge transfer characteristics, and improves the spin-orbit coupling, effectively shortening the exciton lifetime and minimizing the quenching effects.
View Article and Find Full Text PDFBrief Bioinform
July 2025
College of Artificial Intelligence, Nanjing Agricultural University, 666 Binjiang Avenue, Jiangbei New District, Nanjing, Jiangsu Province, 211800, China.
Accurately identifying protein functions is essential to understand life mechanisms and thus advance drug discovery. Although biochemical experiments are the gold standard for determining protein functions, they are often time-consuming and labor-intensive. Here, we proposed a novel composite deep-learning method, Multi-source Knowledge Fusion for Gene Ontology prediction (MKFGO), to infer Gene Ontology (GO) attributes through integrating five complementary pipelines built on multi-source biological data.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
March 2025
The simultaneous use of multiple medications is a common practice in disease treatment, yet the same drug combination can lead to different effects under varying physiological, pharmacological, or genomic conditions-collectively referred to as the 'context'. Accurately predicting the outcomes of drug combinations across diverse contexts, also known as drug relational learning (DRL), is essential for improving therapeutic efficacy and safety. Despite its importance, existing methods face two major challenges: they are often tailored to specific DRL tasks, lacking generalizability, and they fail to explicitly model the influence of context on drug interactions.
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