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1. Enhancement of voltage controlled magnetic anisotropy (VCMA) through electron depletion

   https://doi.org/10.1063/5.0086768  

   Peterson, Thomas J.; Hurben, Anthony; Jiang, Wei; Zhang, Delin; Zink, Brandon; Chen, Yu-Chia; Fan, Yihong; Low, Tony; Wang, Jian-Ping (April 2022, Journal of Applied Physics)

   Recent advancement in the switching of perpendicular magnetic tunnel junctions with an electric field has been a milestone for realizing ultra-low energy memory and computing devices. To integrate with current spin-transfer torque-magnetic tunnel junction and spin–orbit torque-magnetic tunnel junction devices, the typical linear fJ/V m range voltage controlled magnetic anisotropy (VCMA) needs to be significantly enhanced with approaches that include new materials or stack engineering. A possible bidirectional and 1.1 pJ/V m VCMA effect has been predicted by using heavily electron-depleted Fe/MgO interfaces. To improve upon existing VCMA technology, we have proposed inserting high work function materials underneath the magnetic layer. This will deplete electrons from the magnetic layer biasing the gating window into the electron-depleted regime, where the pJ/V m and bidirectional VCMA effect was predicted. We have demonstrated tunable control of the Ta/Pd(x)/Ta underlayer's work function. By varying the Pd thickness (x) from 0 to 10 nm, we have observed a tunable change in the Ta layer's work function from 4.32 to 4.90 eV. To investigate the extent of the electron depletion as a function of the Pd thickness in the underlayer, we have performed DFT calculations on supercells of Ta/Pd(x)/Ta/CoFe/MgO, which demonstrate that electron depletion will not be fully screened at the CoFe/MgO interface. Gated pillar devices with Hall cross geometries were fabricated and tested to extract the anisotropy change as a function of applied gate voltage for samples with various Pd thicknesses. The electron-depleted Pd samples show three to six times VCMA improvement compared to the electron accumulated Ta control sample. 

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2. Engineering Tunneling Selector to Achieve High Non-linearity for 1S1R Integration

   https://doi.org/10.3389/fnano.2021.656026  

   Upadhyay, Navnidhi K.; Blum, Thomas; Maksymovych, Petro; Lavrik, Nickolay V.; Davila, Noraica; Katine, Jordan A.; Ievlev, A. V.; Chi, Miaofang; Xia, Qiangfei; Yang, J. Joshua (April 2021, Frontiers in Nanotechnology)

   Memristor devices have been extensively studied as one of the most promising technologies for next-generation non-volatile memory. However, for the memristor devices to have a real technological impact, they must be densely packed in a large crossbar array (CBA) exceeding Gigabytes in size. Devising a selector device that is CMOS compatible, 3D stackable, and has a high non-linearity (NL) and great endurance is a crucial enabling ingredient to reach this goal. Tunneling based selectors are very promising in these aspects, but the mediocre NL value limits their applications in large passive crossbar arrays. In this work, we demonstrated a trilayer tunneling selector based on the Ge/Pt/TaN 1+x /Ta 2 O 5 /TaN 1+x /Pd layers that could achieve a NL of 3 × 10 5 , which is the highest NL achieved using a tunnel selector so far. The record-high tunneling NL is partially attributed to the bottom electrode's ultra-smoothness (BE) induced by a Ge/Pt layer. We further demonstrated the feasibility of 1S1R (1-selector 1-resistor) integration by vertically integrating a Pd/Ta 2 O 5 /Ru based memristor on top of the proposed selector. 

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3. Preparing preservice mathematics teachers to teach for social justice

   https://doi.org/10.1111/ssm.12602  

   Cahill, Jodie; Bostic, Jonathan D. (November 2023, School Science and Mathematics)

   Abstract This study describes preservice teachers' beliefs about teaching mathematics for social justice (TMSJ) after experiencing a two‐part professional development (PD) workshop on the subject. The research question is: To what degree does a PD experience, designed by a preservice teacher, influence preservice teachers' beliefs about TMSJ? The lead author is a preservice teacher who designed and enacted two workshops for education majors at Midwest University. Results indicated that preservice teachers' beliefs were statistically significantly different, with their beliefs trending toward “more strongly agree” about TMSJ after the two‐part PD. There was also less variance in their responses to the survey after the PD compared to before it. This research offers (a) a narrative of preservice teacher‐driven PD and (b) a rich description of a PD for preservice teachers, thus adding to prior literature about in‐service teachers' outcomes. 

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4. TA‐RNN: an Attention‐based Time‐aware Recurrent Neural Network Architecture to Predict Progression of Alzheimer’s Disease

   https://doi.org/10.1002/alz.089010  

   Olaimat, Mohammad Al; Bozdag, Serdar; Saeed, Fahad (December 2024, Alzheimer's & Dementia)

   Abstract BackgroundAlzheimer’s Disease (AD) is a widespread neurodegenerative disease with Mild Cognitive Impairment (MCI) acting as an interim phase between normal cognitive state and AD. The irreversible nature of AD and the difficulty in early prediction present significant challenges for patients, caregivers, and the healthcare sector. Deep learning (DL) methods such as Recurrent Neural Networks (RNN) have been utilized to analyze Electronic Health Records (EHR) to model disease progression and predict diagnosis. However, these models do not address some inherent irregularities in EHR data such as irregular time intervals between clinical visits. Furthermore, most DL models are not interpretable. To address these issues, we developed a novel DL architecture called Time‐Aware RNN (TA‐RNN) to predict MCI to AD conversion at the next clinical visit. MethodTA‐RNN comprises of a time embedding layer, attention‐based RNN, and prediction layer based on multi‐layer perceptron (MLP) (Figure 1). For interpretability, a dual‐level attention mechanism within the RNN identifies significant visits and features impacting predictions. TA‐RNN addresses irregular time intervals by incorporating time embedding into longitudinal cognitive and neuroimaging data based on attention weights to create a patient embedding. The MLP, trained on demographic data and the patient embedding predicts AD conversion. TA‐RNN was evaluated on Alzheimer’s Disease Neuroimaging Initiative (ADNI) and National Alzheimer’s Coordinating Center (NACC) datasets based on F2 score and sensitivity. ResultMultiple TA‐RNN models were trained with two, three, five, or six visits to predict the diagnosis at the next visit. In one setup, the models were trained and tested on ADNI. In another setup, the models were trained on the entire ADNI dataset and evaluated on the entire NACC dataset. The results indicated superior performance of TA‐RNN compared to state‐of‐the‐art (SOTA) and baseline approaches for both setups (Figure 2A and 2B). Based on attention weights, we also highlighted significant visits (Figure 3A) and features (Figure 3B) and observed that CDRSB and FAQ features and the most recent visit had highest influence in predictions. ConclusionWe propose TA‐RNN, an interpretable model to predict MCI to AD conversion while handling irregular time intervals. TA‐RNN outperformed SOTA and baseline methods in multiple experiments. 

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5. An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data

   https://doi.org/10.3791/65519  

   Hamburger, Robert; Rumble, Christopher; Young, Elizabeth R. (February 2024, Journal of Visualized Experiments)

   Transient absorption (TA) spectroscopy is a powerful time-resolved spectroscopic method used to track the evolution of excited-state processes through changes in the system's absorption spectrum. Early implementations of TA were confined to specialized laboratories, but the evolution of commercial turn-key systems has made the technique increasingly available to research groups across the world. Modern TA systems are capable of producing large datasets with high energetic and temporal resolution that are rich in photophysical information. However, processing, fitting, and interpreting TA spectra can be challenging due to the large number of excited-state features and instrumental artifacts. Many factors must be carefully considered when collecting, processing, and fitting TA data in order to reduce uncertainty over which model or set of fitting parameters best describes the data. The goal of data preparation and fitting is to reduce as many of these extraneous factors while preserving the data for analysis. In this method, beginners are provided with a protocol for processing and preparing TA data as well as a brief introduction to selected fitting procedures and models, specifically single wavelength fitting and global lifetime analysis. Commentary on a number of commonly encountered data preparation challenges and methods of addressing them is provided, followed by a discussion of the challenges and limitations of these simple fitting methods. 

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