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Deformation parameter changes in fission mass yields within the systematic statistical scission-point model
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作者 Payam Mehdipour Kaldiani 《Communications in Theoretical Physics》 SCIE CAS CSCD 2021年第7期95-105,共11页
The fission fragment mass-yields are evaluated for pre-actinide and actinide isotopes using a systematic statistical scission point model.The total potential energy of the fissioning systems at the scission point is p... The fission fragment mass-yields are evaluated for pre-actinide and actinide isotopes using a systematic statistical scission point model.The total potential energy of the fissioning systems at the scission point is presented in approximate relations as functions of mass numbers,deformation parameters and the temperature of complementary fission fragments.The collective temperature,Tcoll,and the temperature of fission fragments,Ti,are separated and the effect of collective temperature on mass yields results is investigated.The fragment temperature has been calculated with the generalized superfluid model.The sum of deformation parameters of complementary fission fragments has been obtained by fitting the calculated results with the experimental data.To investigate the transitions between symmetric and asymmetric modes mass yields for pre-actinide and heavy actinides are calculated with this model.The transition from asymmetric to symmetric fission is well reproduced using this systematic statistical scission point model.The calculated results are in good agreement with the experimental data with Tcoll=2 Me V at intermediate excitation energy and with T_(coll)=1MeV for spontaneous fission.Despite the Langevin model,in the scission point model,a constraint on the deformation parameters of fission fragments has little effect on the results of the mass yield. 展开更多
关键词 mass yields deformation parameters statistical scission point model symmetric and asymmetric modes fission fragments
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Multimodal fiber antenna for proximity and stress sensing
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作者 Merve Gokce Eilam Smolinsky +3 位作者 Louis Alexandre van der Elst Jillian Noblet Creasy Clauser Huntsman Alexander Gumennik 《Advanced Photonics Nexus》 2024年第5期110-119,共10页
Fiber sensors are commonly used to detect environmental,physiological,optical,chemical,and biological factors.Thermally drawn fibers offer numerous advantages over other commercial products,including enhanced sensitiv... Fiber sensors are commonly used to detect environmental,physiological,optical,chemical,and biological factors.Thermally drawn fibers offer numerous advantages over other commercial products,including enhanced sensitivity,accuracy,improved functionality,and ease of manufacturing.Multimaterial,multifunctional fibers encapsulate essential internal structures within a microscale fiber,unlike macroscale sensors requiring separate electronic components.The compact size of fiber sensors enables seamless integration into existing systems,providing the desired functionality.We present a multimodal fiber antenna monitoring,in real time,both the local deformation of the fiber and environmental changes caused by foreign objects in proximity to the fiber.Time domain reflectometry propagates an electromagnetic wave through the fiber,allowing precise determination of spatial changes along the fiber with exceptional resolution and sensitivity.Local changes in impedance reflect fiber deformation,whereas proximity is detected through alterations in the evanescent field surrounding the fiber.The fiber antenna operates as a waveguide to detect local deformation through the antisymmetric mode and environmental changes through the symmetric mode.This multifunctionality broadens its application areas from biomedical engineering to cyber-physical interfacing.In antisymmetric mode,the device can sense local changes in pressure,and,potentially,temperature,pH,and other physiological conditions.In symmetric mode,it can be used in touch screens,environmental detection for security,cyber-physical interfacing,and human-robot interactions. 展开更多
关键词 fiber antenna waveguide symmetric and antisymmetric modes environmental proximity sensing local physiological sensing real-time monitoring
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