Results 211 to 220 of about 317,927 (261)

Improving UV Stability of SiO₂/SiN_x-Passivated Silicon Photodiodes Through Shallow Junction Implantation and Oxide Regrowth. [PDF]

Sensors (Basel)
Getz MN, Koybasi O, Edhborg F, Nordseth Ø, Hesse S, Pohl T, Povoli M, Källberg S, Werner L, Ikonen E, Gran J.   +10 more
europepmc   +1 more source

Flexoelectricity in Photoconversion: Fundamentals, Materials, and Outlooks

Advanced Science, EarlyView.
Mechanical bending of a flexible cantilever induces a strain gradient in the photoactive material. The resulting flexoelectric field couples with photovoltaic and photoconductive effects, modulating charge generation, separation, and collection. A comparative analysis of oxide perovskites, halide perovskites, and two‐dimensional materials is presented,
Xiang Huang, Feng Li, Rongkun Zheng
wiley   +1 more source

The adaptive landscapes of three global <i>Escherichia coli</i> transcriptional regulators. [PDF]

Elife
Westmann CA, Goldbach L, Wagner A.
europepmc   +1 more source

Neuromorphic Near‐Sensor and In‐Sensor Computing Enabled by Next‐Generation Material‐Based Sensors

Advanced Science, EarlyView.
This Review presents a structural framework that classifies neuromorphic sensing into near‐sensor and in‐sensor architectures, clarifying physical coupling between sensing and computation. The framework connects neural and synaptic device functions with recent advances in optical, mechanical, and chemical sensing, compares energy consumption and ...
Su Yeon Jung, Gwang Ya Kim, Sejin Kim, Hyunjong Seo, Se Gi Lee, Sang Min Won, Dongjoon Rhee, Joohoon Kang   +7 more
wiley   +1 more source

Benchmark evaluation in task and motion planning using iteratively deepened AND/OR graph networks. [PDF]

Front Robot AI
Karami H, Thomas A, Mastrogiovanni F.
europepmc   +1 more source

Condition‐Associated Pattern Extraction and Recovery From Multi‐Condition Single‐Cell RNA‐seq Data With CAPER

Advanced Science, EarlyView.
Decoupling biological signals from unwanted variation in multi‑condition single‑cell RNA sequencing data remains challenging. CAPER disentangles condition‑associated biological effects from sample heterogeneity through matrix factorization, producing interpretable latent factors and a batch‑corrected expression matrix.
Ye Li, Jin Ning, An Wang, Minxi Shi, Yuanze Chen, Guoliang Liu, Shiquan Sun   +6 more
wiley   +1 more source

Trafficking and localization of Golgi-resident <i>N</i>-glycan processing enzymes in plants. [PDF]

Front Plant Sci
Dünser K, Schoberer J.
europepmc   +1 more source

Advancing the Design of High‐Efficiency Printable Hole‐Conductor‐Free Mesoscopic Perovskite Solar Cells Through Machine Learning

Advanced Science, EarlyView.
Based on the largest printable mesoscopic perovskite solar cells database we established, stacking model achieved precise PCE prediction (R2 = 0.73, MAE = 2.18%). Multiple experiments verified the accuracy of the model, which guided the fabrication of high‐PCE devices with an efficiency of 19.36%.
Hao Meng, Jingzi Zhang, Xu Zhu, Yuelin Wang, Antai Yang, Kailong Hu, Chengquan Zhong, Jiakai Liu, Menghan Dun, Xi Lin   +9 more
wiley   +1 more source

A 98-qubit trapped-ion quantum computer with all-to-all connectivity. [PDF]

Nature
Ransford A, Allman MS, Arkinstall J, Campora JP, Cooper SF, Delaney RD, Dreiling JM, Estey B, Figgatt C, Hall A, Husain AA, Isanaka A, Kennedy CJ, Kotibhaskar N, Madjarov IS, Mayer K, Milne AR, Park AJ, Reed AP, Ancona R, Andersen MP, Andres-Martinez P, Angenent W, Argueta L, Arkin B, Ascarrunz L, Baker W, Barnes C, Bartolotta J, Berg J, Besand R, Bjork B, Blain M, Blanchard P, Blume-Kohout R, Bohn M, Borgna A, Botamanenko DY, Boutelle R, Brown N, Buckingham GT, Burdick NQ, Burton WC, Carey V, Carron CJ, Chambers J, Chan JW, Children J, Colussi VE, Crepinsek S, Cureton A, Davies J, Davis D, DeCross M, Deen D, Delaney C, DelVento D, DeSalvo BJ, Dominy J, Drotar S, Duncan R, Eccles V, Edgington A, Erickson N, Erickson S, Ertsgaard CT, Esposito J, Evans B, Evans T, Fabrikant MI, Fischer A, Foltz C, Foss-Feig M, Francois D, Freyberg B, Gao C, Garay R, Garvin J, Gaudiosi DM, Gilbreth CN, Giles J, Glynn E, Graves J, Hansen A, Hayes D, Heidemann L, Higashi B, Hilbun T, Hines J, Hlavaty A, Hoffman K, Hoffman IM, Holliman C, Hooper I, Horning B, Hostetter J, Hothem D, Houlton J, Hout J, Hutson R, Jacobs RT, Jacobs T, Johannsen M, Johansen J, Jones L, Julian S, Jung R, Keay A, Klein T, Koch M, Kondo R, Kong C, Kosto A, Lawrence A, Liefer D, Lollie M, Lucchetti D, Lysne NK, Lytle C, MacPherson C, Malm A, Mather S, Mathewson B, Maxwell D, McCaffrey L, McDougall H, Mendoza R, Miller DB, Mills M, Morrison R, Narmour L, Nguyen N, Nugent L, Olson S, Ouellette D, Parks J, Peters Z, Peterson TA, Petricka J, Pino JM, Polito F, Potter AC, Preidl M, Price G, Proctor T, Pugh M, Ratcliff N, Raymondson D, Rhodes P, Roman C, Roy C, Ryan-Anderson C, Sanchez FB, Sangiolo G, Sawadski T, Schaffer A, Schow P, Sedlacek J, Semenenko H, Shevchuk P, Shore S, Siegfried P, Singhal K, Sivarajah S, Skripka T, Sletten L, Spaun B, Sprenkle RT, Stoufer P, Tader M, Taylor SF, Thompson TH, Tobey R, Tran A, Tran T, Vittorini G, Volin C, Walker J, White S, Williams GR, Wilson D, Wolf Q, Wringe C, Young K, Zheng J, Zuraski K, Baldwin CH, Chernoguzov A, Gaebler JP, Sanders SJ, Neyenhuis B, Stutz R, Bohnet JG.   +192 more
europepmc   +1 more source

Ovarian Cancer Diagnosis and Chemoresistance Prediction Model Based on cfRNA Molecular Signature

Advanced Science, EarlyView.
A deep learning model analyzes cfRNA profiles extracted from the blood of OVCA patients. This innovative approach distinguishes OVCA from healthy controls with high accuracy. Crucially, it reliably predicts patient response to chemotherapy (sensitive versus resistant subgroups).
Qinhao Guo, Yangyang Zhang, Yongcheng Jin, Siwei Deng, Yongqi Chen, Zheng Feng, Hao Wen, Liu Wang, Yilin Li, Fanghong Ou, Yong Shen, Haiming Li, Tianyao Zhou, Xingzhu Ju, Xiaohua Wu   +14 more
wiley   +1 more source