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Typical fabrication methods used for laboratory-scale (&lt;1 cm2) investigations of perovskite solar cells do not scale to large-area (&gt;100 cm2) modules required for commercial application- with crystallization control of the active perovskite absorber being particularly challenging. Here, we show that the addition of alkali salts Cs+ and K+ to the perovskite precursor ink used in the scalable slot-die coating can be exploited to control the crystallization of Cs0.15FA0.85Pb(I0.83Br0.17)3 (CsFA), allowing for large-area fabrication of homogeneous and highly crystalline perovskite films. X-ray photoelectron spectroscopy analysis reveals segregation of potassium ions (K+) at the SnO2/perovskite interface, confirming that the additive initially forms nanoclusters in the precursor solution which serves as nucleation sites for subsequent bottom-up crystallization of the perovskite. The highest efficiencies are achieved with the K+ additive: the champion small cell PCE is 18.94% (0.09 cm2) and the mini-module efficiency is 16.22 % at an active area of 57.5 cm2- the highest efficiencies achieved for slot-die coated methylammonium-free perovskites. The small area devices show significantly enhanced operational stability compared to additive-free controls retaining ~72 % of its initial PCE after 426 days (10,224 h) in 30 % relative humidity (RH) and retaining 82% and 54 % of initial PCE after 1,150 h and 958 h at 65 °C and 85 °C. Later, K(add)-CsFA additive based perovskite minimodule retains ~82% of its initial PCE after 200 days (4,800 h) under 30 % RH and the perovskite solar module retains ~50 % of its initial PCE after 560 h without encapsulation under continuous illumination in the dry air environment. We believe that such a controlled “bottom-up crystallization” approach for perovskite film formation is a promising approach for scalability and reproducibility, key for industrializing perovskite photovoltaic technology.

MATERIALS FOR HUMANITY (MH 21)

Functional additive approach for highly efficient slot-die coated perovskite solar cells

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Typical fabrication methods used for laboratory-scale (<1 cm2) investigations of perovskite solar cells do not scale to large-area (>100 cm2) modules required for commercial application- with crystallization control of the active perovskite absorber being particularly challenging. Here, we show that the addition of alkali salts Cs+ and K+ to the perovskite precursor ink used in the scalable slot-die coating can be exploited to control the crystallization of Cs0.15FA0.85Pb(I0.83Br0.17)3 (CsFA), allowing for large-area fabrication of homogeneous and highly crystalline perovskite films. X-ray photoelectron spectroscopy analysis reveals segregation of potassium ions (K+) at the SnO2/perovskite interface, confirming that the additive initially forms nanoclusters in the precursor solution which serves as nucleation sites for subsequent bottom-up crystallization of the perovskite. The highest efficiencies are achieved with the K+ additive: the champion small cell PCE is 18.94% (0.09 cm2) and the mini-module efficiency is 16.22 % at an active area of 57.5 cm2- the highest efficiencies achieved for slot-die coated methylammonium-free perovskites. The small area devices show significantly enhanced operational stability compared to additive-free controls retaining ~72 % of its initial PCE after 426 days (10,224 h) in 30 % relative humidity (RH) and retaining 82% and 54 % of initial PCE after 1,150 h and 958 h at 65 °C and 85 °C. Later, K(add)-CsFA additive based perovskite minimodule retains ~82% of its initial PCE after 200 days (4,800 h) under 30 % RH and the perovskite solar module retains ~50 % of its initial PCE after 560 h without encapsulation under continuous illumination in the dry air environment. We believe that such a controlled “bottom-up crystallization” approach for perovskite film formation is a promising approach for scalability and reproducibility, key for industrializing perovskite photovoltaic technology.

technical paper

Simultaneous improvement of composite strength and toughness has been being in challenge for material researchers for more than a few decades. With natural inspiration and understanding of the nanoscale structure-processing-property relationships, this work presents polymer nanocomposites which were incorporated with the newly-developed filler, composed of three parts synergistically function to improve the mechanical performance of the composite. The filler was designed with (a) the rigid part as inner core and (b) the soft-ductile segment which firmly bonds to the inner core as a middle layer and terminated with (c) the active functional groups, strongly bonding to matrix system. The developed filler showed the synergetic effect of each segment to enhance both strength and toughness of the nanocomposite. One of the studied system is epoxy composite; with only 1 wt% of the newly-developed filler in the epoxy matrix, the impact resistance and the KIc respectively increases ~80%, and ~20%, as compared to the neat epoxy resin. The elongation at break and the maximum strength increased more than 100% and ~50%, respectively. The mechanism of mechanical enhancement was also investigated. The phenomenon indicates that the newly-invented filler can solve the challenge mentioned above and it can be an excellent alternative for the future advanced material industry.

Core-Shell Fillers for Simultaneous Enhancement of Mechanical Performance of Polymer Composites

Electronic structures and complex optical constants including dielectric functions, complex optical conductivities and absorption coefficients of a series of CdSe-based nanoplatelets (NPLs) films are simultaneously unveiled by spectroscopic ellipsometry techniques. Based on our experimental results, the incremental monolayer-thickness in thicker NPLs not only boosts the complex optical constants but also enhances the amplitude of coupling strength of the lowest excitonic transition mainly due to the higher inorganic volume fraction and the improved surface passivation. Overall, the broad tunability of complex optical features by simply controlling the growth of vertical thickness will pave a new path for the design of the next-generation NPLs-containing photonics devices.

Unraveling of Complex Optical Features of Neat Cadmium Selenide Nanoplatelets Thin Films for Next-generation Photonic Devices

Recent years witnessed a significant volume of research for understanding the simple physics in photodetection and its applications in energy and sustainability. Photodetectors are useful in variety of applications from consumer electronic devices to industrial and environmental monitoring, flame and gas sensing, space communication, proximity sensors, missile technology etc to name a few. The possibility of engineerable band gap brought semiconducting TMDC MoS2 as one of the prominent among the 2D layered material family for photodetection and gas sensing applications. In my presentation, I will explain the structural and optical features of the MoS2/AlN heterostructure device fabricated through pulsed laser ablation. A few layers of MoS2 were grown on commercially procured and AlN templated Si wafer in an evacuated PLD chamber by ablating locally prepared MoS2 target with KrF excimer laser. Both photovoltaic and photothermal responses are to be analyzed to account for the observed intensity-modulated and wavelength-dependent photoswitching in MoS2/AlN heterostructures. The self-powered response obtained in the device was attributed to the large difference in band gap values of the complementary materials in the binary system, their band alignment, and band bending. Raman spectrum analysis, XPS, XRR, photodetection, etc will be presented to reveal the physical features and structural characteristics of the heterostructure. A detailed account of the photodetection and switching mechanism will also be discussed. 
Acknowledgment
Pius acknowledges the research grant TARE (TAR/2020/000241) by Science and Engineering Research Board (SERB), DST, Govt. of India to carry out research at Material Research Centre, Indian Institute of Science Bangalore.
Reference
[1] Clark, Jenny, and Guglielmo Lanzani. Nature Photonics 4, no. 7 (2010): 438-446.
[2] Fang, Huajing, Cheng Zheng, Liangliang Wu, Yi Li, Jian Cai, Mingxiang Hu, Xiaosheng Fang, Rong Ma, Qing Wang, and Hong Wang. Advanced Functional Materials 29, no. 20 (2019): 1809013.
[3]Lu, Xiaowei, Peng Jiang, and Xinhe Bao. Nature Communications 10, no. 1 (2019): 1-7.
[4][Singh Deependra Kumar, Rohit Pant, Arun Malla Chowdhury, Basanta Roul, Karuna Kar Nanda, and Saluru Baba Krupanidhi. ACS Applied Electronic Materials 2, no. 4 (2020): 944-953.


Photoswitching and self-powered response in PLD grown MoS2/AlN heterostructure photodetector

To date, many different techniques have been developed to fulfill the increasing demand for 3D representation of information, e.g. in optical security, and virtual reality applications. While holography provides both amplitude and phase information for the reconstruction of the 3D light field, it requires coherent laser sources and suffers from speckles. Here, we developed a simple color-filtering microlens to control both the propagating phase and colors simultaneously, working with incoherent white light sources. Fabricated by two-photon polymerization (2PP) 3D laser printing, our color-filtering microlenses can achieve colorful focal spots at different “floating heights”, when illuminated with incoherent white light source. Meanwhile, it appears like a color prints consisting of bright colorful spots when inspected directly under common microscopy. By arranging the floating colorful spots in 3D space, we designed two metasurfaces, showing the radially and spirally unfolded “optical fireworks” when continuously tunning the focus of microscope. Due to the flexibility in controlling the propagating phase, spectrum, and “floating heights”, our color-filtering microlenses could find broad applications in passive optical security labels and conformal metasurfaces.

Color-filtering Microlenses for colorful 3D display

MoS2 is a promising layered semiconducting material, suitable for electronic and optoelectronic applications due to its remarkable properties such as inherent bandgap and high mobility. However, excessive reverse bias leakage current in mechanically exfoliated MoS2 devices remains a challenge in developing electronic devices. In this work, we studied the mechanism determining reverse bias leakage current behavior in MoS2|ZnO n-n+ isotype heterostructure diodes, fabricated using multilayer MoS2 flakes obtained from two different sources, namely natural and synthetic crystals. These devices are characterized by using temperature dependent J-V characteristics in the temperature range between 145 K to 300 K as shown in Figure 1(a) and (b) for devices made on natural crystals (I-a-N), and Figure 1(c) and (d) for synthetic crystals (I-b-S), respectively. The analysis of temperature dependent J-V characteristics shows that leakage is governed by Poole- Frenkel mechanism. The linear behavior of the plot (b) and (d) confirms that the Poole-Frankel mechanism is operative. This provides evidence that the barrier at the MoS2|ZnO interface is lowered on application of field. The barrier at the interface controls the transport barrier in the structure. The possible role of defects and the band bending at the interface will be discussed. Figure 1: Poole-Frankel analysis of the reverse bias leakage current. (a) and (c) depicts the leakage currents in the device I-a-N (natural MoS2 flake) and I-b-S (synthetic MoS2 flake) devices, respectively. Figure 1 (b) and (d) represents the plot of ln(J/Er) vs. Er(1/2) for I-a-N and I-b-S devices, respectively.

Analysis of leakage current mechanism in isotype MoS2|ZnO heterostructure diodes

Magnetic skyrmions are nanoscale swirling like magnetization textures with topological protection which can be stabilized in ferromagnetic systems with Dzyaloshinskii-Moriya interaction1. Currently, skyrmion are being explored for promising candidate for spintronic based memory and logic applications with low power consumption, fast processing and high density. Ultrafast skyrmion dynamics in GHz-band offers potential for high frequency applications. Recent studies on skyrmion dynamics reveal that there are three types of skyrmion resonant modes: clockwise (CW), counter-clockwise (CCW) gyrotropic modes and breathing mode[2]. In this work, we present a systematic investigation on the microwave properties of the skyrmions in concentric and eccentric ring structures using micromagnetic simulations MuMax3 3. We have varied the inner diameter of concentric ring between 50 to 135 nm and a frequency shift of 2.2 GHz is observed as shown in Fig. 1(a). The frequency of skyrmion resonance mode increases when the diameter of the inner circle is increased. This can be attributed to the fact that the skyrmion experiences edge repulsion from the periphery of both inner and outer circles of the ring geometry. We have observed that the skyrmion is annihilated as the repulsion from the edges suppresses the topological stability of the skyrmion.The skyrmion dynamics are found to be extremely sensitive to the edge repulsions and a remarkably large frequency shift of 2 GHz of the skyrmion resonance modes is observed by simply varying the position of a skyrmion in an eccentric ring structure shown in Fig 1(b). It offers a potential route for two (binary) distinct frequency states by simply driving the skyrmion in different positions of the eccentric ring. Such tunable skyrmion modes can be achieved by driving a skyrmion between different locations using an oscillating magnetic field or current (spin torque effect). Such widely tunable resonant skyrmion modes may have potential implications for the realization of on-chip passive microwave devices such as filter and isolators.

Binary switching of gyrotropic modes of skyrmion in a nano-ring structure

Magnetic multilayer films are known to host a variety of novel magnetic configurations such as chiral domain walls and topological magnetic skyrmions with potential nano-electronic applications. However, characterizing these material systems typically involves a combination of several magnetometry, microscopy, and spectroscopy experiments, and some of these techniques can be time-consuming and expensive. Therefore, to rapidly characterize these material systems, it is crucial to maximize the information extracted from results of experiments that are more accessible, such as domain images obtained from magnetic force microscopy. We show that deep convolutional neural networks can extract magnetic parameters such as the exchange interaction, Dzyaloshinskii-Moriya interaction, and uniaxial anisotropy from images of domain configurations. Experimentally realistic training and validation data were generated through micromagnetic simulations. The trained models were consistently able to reach R2 values greater than 0.7 on validation data. By inspecting the intermediate feature maps of the neural network, we find that the network is able to learn features such as domain boundaries. Testing the models on actual experimental data yield values that were consistent with our knowledge of the material systems. Our work thus demonstrates the utility of developing machine models trained on simulation data as a means to accelerate the characterization of magnetic systems.

Estimation of magnetic parameters from domain images with convolutional neural networks

Demand for Artificial Intelligence (AI) has been growing due to their usefulness in commercial electronics and in tackling global issues, such as a pandemic. However, conventional von Neumann devices that use AI require extensive programming and power. Thus, significant attention is dedicated to the brain-inspired neuromorphic computing (NC) as a more power-efficient alternative and a suitable platform for AI. NC involves a network of interconnected synapses and neurons that mimic their biological counterparts. In this work, we present a DW-based artificial neuron that automatically resets without applying current.
We simulated a synthetic antiferromagnetic (SAF) DW device where the free layer (CoFeB) is driven by spin-transfer-torque (STT), achieving the integrate function. In the absence of current, the antiferromagnetic coupling (AFC) between the free and pinned layers pushes the DW back towards the starting position. This happens automatically, and is indicative of the leakage function. The integrate and leakage functions are also achieved experimentally, suggesting that an SAF-based neuron is viable for NC.

Synthetic antiferromagnetic domain wall device as artificial neuron for neuromorphic computing

Descriptor based predictive model can be efficient in identifying an optimal carbon-based catalyst for oxygen evolution reaction (OER). Here, we correlate the O atom adsorption strength with the OER activity of graphene nanoribbon systems and define the energy parameters to identify the overpotential (ɳ). The π electron-based descriptor can predict the catalytic activity of the graphene surfaces. Machine learning algorithms like Multiple Linear Regression (MLR), Random Forest Regression (RFR) and Support Vector Regression (SVR) are trained on the data generated by density functional theory to predict the energy parameter (ΔGO-ΔGOH) and overpotential. An optimal active site for OER using proposed SVR model is identified with the overpotential (0.29 V) and then validate through DFT calculations. Also, to generalize the study we used SVR model on nitrogen doped graphene system to predict the site-specific activity towards OER. Such a combined approach can be extended to estimate the site-specific OER activity of different carbon catalysts at a dramatically reduced computational cost.

Energy Parameter and Electronic Descriptor for Carbon Based Catalyst Predicted using QM/ML

Either experimental trial and error or density functional theory (DFT) calculations for screening electrocatalysts remain comparatively expensive and scale poorly with system size. Here, we develop a simple but accurate binding-energy prediction machine-learning model to accelerate the hydrogen evolution reaction (HER) catalyst screening. Based on around 20 000 DFT-calculated HER binding-energy datasets, we develop a simple machine-learning model with only four features based on the optimized finger mapping method. Our method predicts H binding energies with a mean absolute error (MAE) of 0.14 eV. It is much lower than that from the regression of the Voronoi tessellations-based crystal structure modeling method (0.27 eV) and crystal graph convolutional neural networks (0.19 eV), which requires hundred features or expensive training cost. Moreover, for a small dataset, our explainable model has superior performance to the deep neural network models.

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