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您的检索式：作者名="Muhammad Yasir AKRAM"
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| 1 | A gelatin-based artificial SEI for lithium deposition regulation and polysulfide shuttle suppression in lithium-sulfur batteries显示文摘Lithium-sulfur(Li-S) battery is one of the best candidates for the next-generation energy storage system due to its high theoretical capacity(1675 mA h-1),low cost and environment friendliness.However,lithium(Li) dendrites formation and polysulfide shuttle effect are two major challenges that limit the commercialization of Li-S batteries.Here we design a facile bifunctional interlayer of gelatin-based fibers(GFs),aiming to protect the Li anode surface from the dendrites growth and also hinder the polysulfide shuttle effect.We reveal that the 3D structural network of GFs layer with abundant polar sites helps to homogenize Li-ion flux,leading to uniform Li-ion deposition.Meanwhile,the polar moieties also immobilize the lithium polysulfides and protect the Li metal from the side-reaction.As a result,the anodeprotected batteries have shown significantly enhanced performance.A high coulombic efficiency of 96% after 160 cycles has been achieved in the Li-Cu half cells.The Li-Li symmetric cells exhibit a prolonged lifespan for 800 h with voltage hysteresis(10 mV).With the as-prepared GFs layer,the Li-S battery shows approximately 14% higher capacity retention than the pristine battery at 0.5 C after 100 cycles.Our work presents that this gelatin-based bi-functional interlayer provides a viable strategy for the manufacturing of advanced Li-S batteries. | Naseem Akhtar Xiaogang Sun Muhammad Yasir Akram Fakhar Zaman Weikun Wang Anbang Wang Long Chen Hao Zhang Yuepeng Guan Yaqin Huang | 2021 | Journal of Energy Chemistry2021，30，1： | 5 |
| 2 | Binary phase solid-state photopolymerization of acrylates: design, characterization and biomineralization of 3D scaffolds for tissue engineering显示文摘 | Inamullah MAITLO Safdar ALI Muhammad Yasir AKRAM Farooq Khurum SHEHZAD Jun NIE | 2017 | Frontiers of Materials Science2017，11，4： | 1 |
| 3 | Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning显示文摘Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area. | Muhammad Waleed Pasha Mir Yasir Umair Alina Mirza Faizan Rao Abdul Wakeel Safia Akram Fazli Subhan Wazir Zada Khan | 2021 | Computers, Materials & Continua2021，，11： | 0 |
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