A highly active phosphine-free ruthenium-arene complex,[(η^(6)-C_(6)H_(6))RuCl_(2)(C_(6)H_(5)NH_(2))],exhibits excellent catalytic performance for a one-pot conversion of aldehydes to primary amides at low temperatur...A highly active phosphine-free ruthenium-arene complex,[(η^(6)-C_(6)H_(6))RuCl_(2)(C_(6)H_(5)NH_(2))],exhibits excellent catalytic performance for a one-pot conversion of aldehydes to primary amides at low temperature(60°C),in water and without any inert gas protection.The reported catalyst performed exceptionally well for a huge range of aldehydes,including aromatic,heteroaromatic,aliphatic and conjugated systems,with a high tolerance for other functional groups.The development of such highly active catalysts using simple reagents will offer new opportunities for the development of improved phosphine-free catalytic systems for this and other related catalytic reactions.展开更多
This paper introduces a deep learning(DL)algorithm for estimating doubly-selective fading channel and detecting signals in orthogonal frequency division multiplexing(OFDM)communication systems affected by hardware imp...This paper introduces a deep learning(DL)algorithm for estimating doubly-selective fading channel and detecting signals in orthogonal frequency division multiplexing(OFDM)communication systems affected by hardware impairments(HIs).In practice,hardware imperfections are present at the transceivers,which are modeled as direct current(DC)offset,carrier frequency offset(CFO),and in-phase and quadrature-phase(IQ)imbalance at the transmitter and the receiver in OFDM system.In HIs,the explicit system model could not be mathematically derived,which limits the performance of conventional least square(LS)or minimum mean square error(MMSE)estimators.Thus,we consider time-frequency response of a channel as a 2D image,and unknown values of the channel response are derived using known values at the pilot locations with DL-based image super-resolution,and image restoration techniques.Further,a deep neural network(DNN)is designed to fit the mapping between the received signal and transmit symbols,where the number of outputs equals to the size of the modulation order.Results show that there are no significant effects of HIs on channel estimation and signal detection in the proposed DL-assisted algorithm.The proposed DL-assisted detection improves the OFDM performance as compared to the conventional LS/MMSE under severe Hls.展开更多
We investigate the effect of a high-k dielectric in the tunnel layer to improve the erase speed-retention trade-off. Here, the proposed stack in the tunnel layer is AlLaO_3/Hf AlO/SiO_2. These proposed materials posse...We investigate the effect of a high-k dielectric in the tunnel layer to improve the erase speed-retention trade-off. Here, the proposed stack in the tunnel layer is AlLaO_3/Hf AlO/SiO_2. These proposed materials possess low valence band offset with high permittivity to improve both the erase speed and retention time in barrier engineered silicon-oxide-nitride-oxide-silicon（BE-SONOS）. In the proposed structure Hf Al O and AlLaO_3 replace Si_3N_4 and the top SiO_2 layer in a conventional oxide/nitride/oxide（ONO） tunnel stack. Due to the lower conduction band offset（CBO） and high permittivity of the proposed material in the tunnel layer, it offers better program/erase（P/E） speed and retention time. In this work the gate length is also scaled down from 220 to 55 nm to observe the effect of high-k materials while scaling, for the same equivalent oxide thickness（EOT）. We found that the scaling down of the gate length has a negligible impact on the memory window of the devices. Hence, various investigated tunnel oxide stacks possess a good memory window with a charge retained up to 87.4%（at room temperature） after a period of ten years. We also examine the use of a metal gate instead of a polysilicon gate, which shows improved P/E speed and retention time.展开更多
文摘A highly active phosphine-free ruthenium-arene complex,[(η^(6)-C_(6)H_(6))RuCl_(2)(C_(6)H_(5)NH_(2))],exhibits excellent catalytic performance for a one-pot conversion of aldehydes to primary amides at low temperature(60°C),in water and without any inert gas protection.The reported catalyst performed exceptionally well for a huge range of aldehydes,including aromatic,heteroaromatic,aliphatic and conjugated systems,with a high tolerance for other functional groups.The development of such highly active catalysts using simple reagents will offer new opportunities for the development of improved phosphine-free catalytic systems for this and other related catalytic reactions.
基金supported by the Ministry of Science and Technology,SERB under Grant SRG/2021/000199 and by the Indian National Academy of Engineering(INAE)Project with Sanction under Grant 2023/INTW/10.
文摘This paper introduces a deep learning(DL)algorithm for estimating doubly-selective fading channel and detecting signals in orthogonal frequency division multiplexing(OFDM)communication systems affected by hardware impairments(HIs).In practice,hardware imperfections are present at the transceivers,which are modeled as direct current(DC)offset,carrier frequency offset(CFO),and in-phase and quadrature-phase(IQ)imbalance at the transmitter and the receiver in OFDM system.In HIs,the explicit system model could not be mathematically derived,which limits the performance of conventional least square(LS)or minimum mean square error(MMSE)estimators.Thus,we consider time-frequency response of a channel as a 2D image,and unknown values of the channel response are derived using known values at the pilot locations with DL-based image super-resolution,and image restoration techniques.Further,a deep neural network(DNN)is designed to fit the mapping between the received signal and transmit symbols,where the number of outputs equals to the size of the modulation order.Results show that there are no significant effects of HIs on channel estimation and signal detection in the proposed DL-assisted algorithm.The proposed DL-assisted detection improves the OFDM performance as compared to the conventional LS/MMSE under severe Hls.
文摘We investigate the effect of a high-k dielectric in the tunnel layer to improve the erase speed-retention trade-off. Here, the proposed stack in the tunnel layer is AlLaO_3/Hf AlO/SiO_2. These proposed materials possess low valence band offset with high permittivity to improve both the erase speed and retention time in barrier engineered silicon-oxide-nitride-oxide-silicon（BE-SONOS）. In the proposed structure Hf Al O and AlLaO_3 replace Si_3N_4 and the top SiO_2 layer in a conventional oxide/nitride/oxide（ONO） tunnel stack. Due to the lower conduction band offset（CBO） and high permittivity of the proposed material in the tunnel layer, it offers better program/erase（P/E） speed and retention time. In this work the gate length is also scaled down from 220 to 55 nm to observe the effect of high-k materials while scaling, for the same equivalent oxide thickness（EOT）. We found that the scaling down of the gate length has a negligible impact on the memory window of the devices. Hence, various investigated tunnel oxide stacks possess a good memory window with a charge retained up to 87.4%（at room temperature） after a period of ten years. We also examine the use of a metal gate instead of a polysilicon gate, which shows improved P/E speed and retention time.
