Multilevel Conductance States of Vapor-Transport-Deposited Sb2S3 Memristors Achieved via Electrical and Optical Modulation.

The pursuit of advanced brain-inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb2S3) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin-film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb2S3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb2S3/Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb2S3-based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb2S3 enables the Ag/Sb2S3/Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb2S3 in future memory devices and optoelectronics and in shaping electronics with versatility.

GeTe/MoTe2 Van der Waals Heterostructures: Enabling Ultralow Voltage Memristors for Nonvolatile Memory and Neuromorphic Computing Applications.

Advanced electronic semiconducting Van der Waals heterostructures (HSs) are promising candidates for exploring next-generation nanoelectronics owing to their exceptional electronic properties, which present the possibility of extending their functionalities to diverse potential applications. In this study, GeTe/MoTe2 HS are explored for nonvolatile memory and neuromorphic-computing applications. Sputter-deposited Ag/GeTe/MoTe2/Pt HS cross-point devices are fabricated, and they demonstrate memristor behavior at ultralow switching voltages (VSET: 0.15 V and VRESET: -0.14 V) with very low energy consumption (≈30 nJ), high memory window, long retention time (104 s), and excellent endurance (105 cycles). Resistive switching is achieved by adjusting the interface between the Ag top electrode and the heterojunction switching layer. Cross-sectional transmission electron microscope images and conductive atomic force microscopy analysis confirm the presence of a conducting filament in the heterojunction switching layer. Further, emulating various synaptic functions of a biological synapse reveals that GeTe/MoTe2 HS can be utilized for energy-efficient neuromorphic-computing applications. A multilayer perceptron is implemented using the synaptic weights of the Ag/GeTe/MoTe2/Pt HS device, revealing high pattern accuracy (81.3%). These results indicate that HS devices can be considered a potential solution for high-density memory and artificial intelligence applications.

Synergistic Enhancement of Water-Splitting Performance Using MOF-Derived Ceria-Modified g-C3N4 Nanocomposites: Synthesis, Performance Evaluation, and Stability Prediction with Machine Learning.

Diminishing the charge recombination rate by improving the photoelectrochemical (PEC) performance of graphitic carbon nitride (g-C3N4) is essential for better water oxidation. In this concern, this research explores the competent approach to enhance the PEC performance of g-C3N4 nanosheets (NSs), creating their nanocomposites (NCs) with metal-organic framework (MOF)-derived porous CeO2 nanobars (NBs) along with ZnO nanorods (NRs) and TiO2 nanoparticles (NPs). The synthesis involved preparing CeO2 NBs and g-C3N4 NSs through the calcination of respective precursors, while the sol-gel method is employed for ZnO NRs and TiO2 NPs. Following the subsequent analysis of the physicochemical properties of the materials, the binder-free brush-coating method is deployed to fabricate NC-based photoanodes, followed by an evaluation of the PEC performance through various electrochemical techniques. Remarkably, the binary g-C3N4/CeO2 NCs with 20 wt % CeO2 NBs (gC20 NCs) exhibited a significantly enhanced current density of 0.460 mA/cm2 at 1.23 V vs reversible hydrogen electrode, which is 2.3 times greater than that of bare g-C3N4 NSs (0.195 mA/cm2). Further improvements are observed with ternary gC20/TiO2 (gCT50) and gC20/ZnO (gCZ50) NCs, achieving current densities of 1.810 and 1.440 mA/cm2, respectively. These enhanced current densities are attributed to increased donor densities, reduced charge transfer resistances, and efficient charge transport within the NCs. In addition, higher surface areas with beneficial instinctive defects are perceived for gCT50 and gCZ50 NCs, as revealed by Brunauer-Emmett-Teller and electron spin resonance analysis. Finally, the stability of gCZ50 and gCT50 NC-based photoanodes is predicted and forecasted with the help of the recurrent neural network-based long short-term memory technique. Overall, this study demonstrates the efficacy of organic-inorganic hybrids for efficient photoanodes, facilitating advancements in water-splitting studies.

Multilevel resistive switching in hydrothermally synthesized FeWO4 thin film-based memristive device for non-volatile memory application.

The memristors offer significant advantages as a key element in non-volatile and brain-inspired neuromorphic systems because of their salient features such as remarkable endurance, ability to store multiple bits, fast operation speed, and extremely low energy usage. This work reports the resistive switching (RS) characteristics of the hydrothermally synthesized iron tungstate (FeWO4) based thin film memristive device. The detailed physicochemical analysis was investigated using Rietveld's refinement, X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM) techniques. The fabricated Ag/FWO/FTO memristive device exhibits bipolar resistive switching (BRS) behavior. In addition, the devices exhibit negative differential resistance (NDR) at both positive and negative bias. The charge-flux relation portrayed the non-ideal or memristive nature of the devices. The reliability in the RS process was analyzed in detail using Weibull distribution and time series analysis techniques. The device exhibits stable and multilevel endurance and retention characteristics which demonstrates the suitability of the device for the high-density non-volatile memory application. The current conduction of the device was dominated by Ohmic and trap controlled-space charge limited current (TC-SCLC) mechanisms and filamentary RS process responsible for the BRS in the device. In a nutshell, the present investigations reveal the potential use of the iron tungstate for the fabrication of memristive devices for the non-volatile memory application.

CogniFiber: Harnessing Biocompatible and Biodegradable 1D Collagen Nanofibers for Sustainable Nonvolatile Memory and Synaptic Learning Applications.

Here, resistive switching (RS) devices are fabricated using naturally abundant, nontoxic, biocompatible, and biodegradable biomaterials. For this purpose, 1D chitosan nanofibers (NFs), collagen NFs, and chitosan-collagen NFs are synthesized by using an electrospinning technique. Among different NFs, the collagen-NFs-based device shows promising RS characteristics. In particular, the optimized Ag/collagen NFs/fluorine-doped tin oxide RS device shows a voltage-tunable analog memory behavior and good nonvolatile memory properties. Moreover, it can also mimic various biological synaptic learning properties and can be used for pattern classification applications with the help of the spiking neural network. The time series analysis technique is employed to model and predict the switching variations of the RS device. Moreover, the collagen NFs have shown good cytotoxicity and anticancer properties, suggesting excellent biocompatibility as a switching layer. The biocompatibility of collagen NFs is explored with the help of NRK-52E (Normal Rat Kidney cell line) and MCF-7 (Michigan Cancer Foundation-7 cancer cell line). Additionally, the biodegradability of the device is evaluated through a physical transient test. This work provides a vital step toward developing a biocompatible and biodegradable switching material for sustainable nonvolatile memory and neuromorphic computing applications.

Solvothermal synthesis of TiO2nanospheres for non-volatile memory and synaptic learning applications.

In this study, we used the one-pot solvothermal method to synthesize the TiO2nanospheres (NSs) and used them for non-volatile memory and neuromorphic computing applications. Several analytical tools were used to understand the structural, optical, morphological, and compositional characteristics of synthesized TiO2NSs. The tetragonal crystal structure of anatase TiO2was formed, according to the Rietveld refined x-ray diffraction results. The NS morphology was confirmed by field emission scanning electron microscopy and transmission electron microscopy images. X-ray photoelectron spectroscopy was probed to understand the elemental composition and electronic states of the TiO2NSs. We specifically looked at the impact of reaction time on the structural, optical, morphological, compositional, and resistive switching (RS) properties of TiO2NSs. The fabricated devices (Ag/TiO2NSs/FTO) exhibit bipolar RS behavior. The optimized RS device shows good endurance (5000 cycles) and memory retention (5000 s) properties. Moreover, fabricated devices showed double-valued charge-flux characteristics, whereas charge transport was caused by the Ohmic and space charge-limited current mechanisms. Additionally, the optimized device can mimic various synaptic characteristics including potentiation-depression, excitatory post-synaptic current, and paired-pulse facilitation.

Self-Assembled Lanthanum Oxide Nanoflakes by Electrodeposition Technique for Resistive Switching Memory and Artificial Synaptic Devices.

In recent years, many metal oxides have been rigorously studied to be employed as solid electrolytes for resistive switching (RS) devices. Among these solid electrolytes, lanthanum oxide (La2 O3 ) is comparatively less explored for RS applications. Given this, the present work focuses on the electrodeposition of La2 O3 switching layers and the investigation of their RS properties for memory and neuromorphic computing applications. Initially, the electrodeposited La2 O3 switching layers are thoroughly characterized by various analytical techniques. The electrochemical impedance spectroscopy (EIS) and Mott-Schottky techniques are probed to understand the in situ electrodeposition, RS mechanism, and n-type semiconducting nature of the fabricated La2 O3 switching layers. All the fabricated devices exhibit bipolar RS characteristics with excellent endurance and stable retention. Moreover, the device mimics the various bio-synaptic properties such as potentiation-depression, excitatory post-synaptic currents, and paired-pulse facilitation. It is demonstrated that the fabricated devices are non-ideal memristors based on double-valued charge-flux characteristics. The switching variation of the device is studied using the Weibull distribution technique and modeled and predicted by the time series analysis technique. Based on electrical and EIS results, a possible filamentary-based RS mechanism is suggested. The present results assert that La2 O3 is a promising solid electrolyte for memory and brain-inspired applications.

Review of Electrochemically Synthesized Resistive Switching Devices: Memory Storage, Neuromorphic Computing, and Sensing Applications.

Resistive-switching-based memory devices meet most of the requirements for use in next-generation information and communication technology applications, including standalone memory devices, neuromorphic hardware, and embedded sensing devices with on-chip storage, due to their low cost, excellent memory retention, compatibility with 3D integration, in-memory computing capabilities, and ease of fabrication. Electrochemical synthesis is the most widespread technique for the fabrication of state-of-the-art memory devices. The present review article summarizes the electrochemical approaches that have been proposed for the fabrication of switching, memristor, and memristive devices for memory storage, neuromorphic computing, and sensing applications, highlighting their various advantages and performance metrics. We also present the challenges and future research directions for this field in the concluding section.

Flexible Memristive Organic Solar Cell Using Multilayer 2D Titanium Carbide MXene Electrodes.

Hybrid systems have attracted significant attention within the scientific community due to their multifunctionality, which has resulted in increasing demands for wearable electronics, green energy, and miniaturization. Furthermore, MXenes are promising two-dimensional materials that have been applied in various areas due to their unique properties. Herein, a flexible, transparent, and conductive electrode (FTCE) based on a multilayer hybrid MXene/Ag/MXene structure that can be applied to realize an inverted organic solar cell (OSC) with memory and learning functionalities is reported. This optimized FTCE exhibits high transmittance (84%), low sheet resistance (9.7 Ω sq-1 ), and reliable operation (even after 2000 bending cycles). Moreover, the OSC using this FTCE achieves a power conversion efficiency of 13.86% and sustained photovoltaic performance, even after hundreds of switching cycles. The fabricated memristive OSC (MemOSC) device also exhibits reliable resistive switching behavior at low operating voltages of 0.60 and -0.33 V (similar to biological synapses), an excellent ON/OFF ratio (103 ), stable endurance performance (4 × 103 ), and memory retention properties (104 s). Moreover, the MemOSC device can mimic synaptic functionalities on a biological time scale. Thus, MXene can potentially be used as an electrode for highly efficient OSCs with memristive functions for future intelligent solar cell modules.

Sprayed FeWO4 thin film-based memristive device with negative differential resistance effect for non-volatile memory and synaptic learning applications.

Resistive switching (RS) memories have attracted great attention as promising solutions to next-generation non-volatile memories and computing technologies because of their simple device configuration, high on/off ratio, low power consumption, fast switching, long retention, and significant cyclic stability. In this work, uniform and adherent iron tungstate (FeWO4) thin films were synthesized by the spray pyrolysis method with various precursor solution volumes, and these were tested as a switching layer for the fabrication of Ag/FWO/FTO memristive devices. The detailed structural investigation was done through various analytical and physio-chemical characterizations viz. X-ray diffraction (XRD) and its Rietveld refinement, Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) techniques. The results reveal the pure and single-phase FeWO4 compound thin film formation. Surface morphological study shows the spherical particle formation having a diameter in the range of 20 to 40 nm. The RS characteristics of the Ag/FWO/FTO memristive device demonstrate non-volatile memory characteristics with significant endurance and retention properties. Interestingly, the memory devices show stable and reproducible negative differential resistance (NDR) effects. The in-depth statistical analysis suggests the good operational uniformity of the device. Moreover, the switching voltages of the Ag/FWO/FTO memristive device were modeled using the time series analysis technique by utilizing Holt's Winter Exponential Smoothing (HWES) approach. Additionally, the device mimics bio-synaptic properties such as potentiation/depression, excitatory post-synaptic current (EPSC), and spike-timing-dependent plasticity (STDP) learning rules. For the present device, the space-charge-limited current (SCLC) and trap-controlled-SCLC effects dominated during positive and negative bias I-V characteristics, respectively. The RS mechanism dominated in the low resistance state (LRS), and the high resistance state (HRS) was explained based on the formation and rupture of conductive filament composed of Ag ions and oxygen vacancies. This work demonstrates the RS in the metal tungstate-based memristive devices and demonstrates a low-cost approach for fabricating memristive devices.

Recent progress in energy, environment, and electronic applications of MXene nanomaterials.

Since the discovery of graphene, two-dimensional (2D) materials have gained widespread attention, owing to their appealing properties for various technological applications. Etched from their parent MAX phases, MXene is a newly emerged 2D material that was first reported in 2011. Since then, a lot of theoretical and experimental work has been done on more than 30 MXene structures for various applications. Given this, in the present review, we have tried to cover the multidisciplinary aspects of MXene including its structures, synthesis methods, and electronic, mechanical, optoelectronic, and magnetic properties. From an application point of view, we explore MXene-based supercapacitors, gas sensors, strain sensors, biosensors, electromagnetic interference shielding, microwave absorption, memristors, and artificial synaptic devices. Also, the impact of MXene-based materials on the characteristics of respective applications is systematically explored. This review provides the current status of MXene nanomaterials for various applications and possible future developments in this field.

Flexible Organic-Inorganic Halide Perovskite-Based Diffusive Memristor for Artificial Nociceptors.

With the current evolution in the artificial intelligence technology, more biomimetic functions are essential to execute increasingly complicated tasks and respond to challenging work environments. Therefore, an artificial nociceptor plays a significant role in the advancement of humanoid robots. Organic-inorganic halide perovskites (OHPs) have the potential to mimic the biological neurons due to their inherent ion migration. Herein, a versatile and reliable diffusive memristor built on an OHP is reported as an artificial nociceptor. This OHP diffusive memristor showed threshold switching properties with excellent uniformity, forming-free behavior, a high ION/IOFF ratio (104), and bending endurance over >102 cycles. To emulate the biological nociceptor functionalities, four significant characteristics of the artificial nociceptor, such as threshold, no adaptation, relaxation, and sensitization, are demonstrated. Further, the feasibility of OHP nociceptors in artificial intelligence is being investigated by fabricating a thermoreceptor system. These findings suggest a prospective application of an OHP-based diffusive memristor in the future neuromorphic intelligence platform.

Evaluation of Cellulose-MXene Composite Hydrogel Based Bio-Resistive Random Access Memory Material as Mimics for Biological Synapses.

We report a memory device based on organic-inorganic hybrid cellulose-Ti3C2TX MXene composite hydrogel (CMCH) as a switching layer sandwiched between Ag top and FTO bottom electrodes. The device (Ag/CMCH/FTO) was fabricated by a simple, solution-processed route and exhibits reliable and reproducible bipolar resistive switching. Multilevel switching behavior was observed at low operating voltages (±0.5 to ±1 V). Furthermore, the capacitive-coupled memristive characteristics of the device were corroborated with electrochemical impedance spectroscopy and this affirmed the filamentary conduction switching mechanism (LRS-HRS). The synaptic functions of the CMCH-based memory device were evaluated, wherein potentiation/depression properties over 8 × 103 electric pulses were observed. The device also exhibited spike time-dependent plasticity-based symmetric Hebbian learning rule of a biological synapse. This hybrid hydrogel is expected to be a potential switching material for low-cost, sustainable, and biocompatible memory storage devices and artificial synaptic applications.

Unraveling the importance of fabrication parameters of copper oxide-based resistive switching memory devices by machine learning techniques.

In the present study, various statistical and machine learning (ML) techniques were used to understand how device fabrication parameters affect the performance of copper oxide-based resistive switching (RS) devices. In the present case, the data was collected from copper oxide RS devices-based research articles, published between 2008 to 2022. Initially, different patterns present in the data were analyzed by statistical techniques. Then, the classification and regression tree algorithm (CART) and decision tree (DT) ML algorithms were implemented to get the device fabrication guidelines for the continuous and categorical features of copper oxide-based RS devices, respectively. In the next step, the random forest algorithm was found to be suitable for the prediction of continuous-type features as compared to a linear model and artificial neural network (ANN). Moreover, the DT algorithm predicts the performance of categorical-type features very well. The feature importance score was calculated for each continuous and categorical feature by the gradient boosting (GB) algorithm. Finally, the suggested ML guidelines were employed to fabricate the copper oxide-based RS device and demonstrated its non-volatile memory properties. The results of ML algorithms and experimental devices are in good agreement with each other, suggesting the importance of ML techniques for understanding and optimizing memory devices.

Evaluation of Nanostructured NiS2 Thin Films from a Single-Source Precursor for Flexible Memristive Devices.

Herein, we report the first demonstration of a single-step, in situ growth of NiS2 nanostructures from a single-source precursor onto a flexible substrate as a versatile platform for an effective nonvolatile memristor. The low temperature, solution-processed deposition of NiS2 thin films exhibits a wide band gap range, spherical-flower-like morphology with high surface area and porosity, and negligible surface roughness. Moreover, the fabricated Au/NiS2/ITO/PET memristor device reveals reproducible bipolar resistive switching (RS) at low operational voltages under both flat and bending conditions. The flexible device shows stable RS behavior for multiple cycles with a good memory window (∼102) and data retention of up to 104 s. The switching of a device between a high-resistance state and a low-resistance state is attributed to the filamentary conduction based on sulfur ion migration and sulfur vacancies and plays a key role in the outstanding memristive performance of the device. Consequently, this work provides a simple, scalable, solution-processed route to fabricate a flexible device with potential applications in next-generation neuromorphic computing and wearable electronics.

Tetraphenylethene-Based Fluorescent Chemosensor with Mechanochromic and Aggregation-Induced Emission (AIE) Properties for the Selective and Sensitive Detection of Hg2+ and Ag+ Ions in Aqueous Media: Application to Environmental Analysis.

It is critical to design a novel and simple bifunctional sensor for the selective and sensitive detection of ions in an aqueous media in environmental samples. As a result, in this study, tetraphenylethene hydrazinecarbothioamide (TPE-PVA), known as probe 1, was successfully synthesized and characterized as having impressive photophysical phenomena such as aggregation-induced emission (AIE) and mechanochromic properties by applying mechanical force to the solid of probe 1. The emission of the solid of probe 1 changed from turquoise blue to lemon yellow after grinding, from lemon yellow to parakeet green after annealing at 160 °C, and to arctic blue after fuming with DCM. Such characteristics could lead to a variety of applications in several fields. The probe was implemented and demonstrated remarkable selectivity and sensitivity toward mercury(II) and silver(I) ions by substantially switching off emission over other cations. Following an extensive photophysical analysis, it was discovered that detection limits (LOD) as low as 0.18344 and 0.2384 µg mL-1 for Hg2+ and Ag+, respectively, are possible with a quantum yield (Φ) of 2.26. Probe 1 was also explored as a Hg2+ and Ag+ paper strip-based sensor and kit for practical use. The binding mechanisms of probe 1 (TPE-PVA) with Hg2+ and Ag+ were confirmed by 1H NMR titration. These results could lead to the development of reliable onsite Hg2+ and Ag+ fluorescent probes in the future.

Unraveling the Effect of the Water Content in the Electrolyte on the Resistive Switching Properties of Self-Assembled One-Dimensional Anodized TiO2 Nanotubes.

The applied potential, time, and water content are crucial factors in the electrochemical anodization process because the growth of one-dimensional nanotubes can be accelerated by enhancing the corrosive effect. We investigated the effect of the water content on the resistive switching (RS) properties of Ti foils by anodizing the foils and varying the water content in an electrolyte (1-10 vol %). By increasing the water content, we facilitated a slow transition from nanopores to nanotubes and realized an increase in the tube wall diameter and tube length. All of the fabricated memristive devices exhibited a reliable and reproducible bipolar resistive switching effect. The optimized device exhibited bipolar RS properties with good dc endurance (104 cycles) and data retention capability (105 s). Our results suggest that as the water content increases to 5 vol %, the RS process improves; further increases in the water content impair the RS process.

Amorphous Boron Nitride Memristive Device for High-Density Memory and Neuromorphic Computing Applications.

Although two-dimensional (2D) nanomaterials are promising candidates for use in memory and synaptic devices owing to their unique physical, chemical, and electrical properties, the process compatibility, synthetic reliability, and cost-effectiveness of 2D materials must be enhanced. In this context, amorphous boron nitride (a-BN) has emerged as a potential material for future 2D nanoelectronics. Therefore, we explored the use of a-BN for multilevel resistive switching (MRS) and synaptic learning applications by fabricating a complementary metal-oxide-semiconductor (CMOS)-compatible Ag/a-BN/Pt memory device. The redox-active Ag and boron vacancies enhance the mixed electrochemical metallization and valence change conduction mechanism. The synthesized a-BN switching layer was characterized using several analyses. The fabricated memory devices exhibited bipolar resistive switching with low set and reset voltages (+0.8 and -2 V, respectively) and a small operating voltage distribution. In addition, the switching voltages of the device were modeled using a time-series analysis, for which the Holt's exponential smoothing technique provided good modeling and prediction results. According to the analytical calculations, the fabricated Ag/a-BN/Pt device was found to be memristive, and its MRS ability was investigated by varying the compliance current. The multilevel states demonstrated a uniform resistance distribution with a high endurance of up to 104 direct current (DC) cycles and memory retention characteristics of over 106 s. Conductive atomic force microscopy was performed to clarify the resistive switching mechanism of the device, and the likely mixed electrochemical metallization and valence change mechanisms involved therein were discussed based on experimental results. The Ag/a-BN/Pt memristive devices mimicked potentiation/depression and spike-timing-dependent plasticity-based Hebbian-learning rules with a high pattern accuracy (90.8%) when implemented in neural network simulations.

Analysis and Prediction of Hydrothermally Synthesized ZnO-Based Dye-Sensitized Solar Cell Properties Using Statistical and Machine-Learning Techniques.

Dye-sensitized solar cells (DSSCs) are one of the most versatile and low-cost solar cells. However, DSSCs are prone to low power conversion efficiency (PCE) compared to their counterparts, owing to their different synthesis parameters and process conditions. Therefore, designing efficient DSSCs and identifying the parameters that control the PCE of DSSCs are a critical tasks. We have collected data from hydrothermally synthesized DSSCs in the present work, published from 2005 to 2020. In line with publishing trends in the said period, we evaluate ZnO as a popular photoactive material for DSSC applications. We further analyzed the performance of hydrothermally synthesized ZnO DSSCs using different statistical techniques and provided some significant insights. We further applied the machine-learning technique with a decision tree algorithm to understand and discover the possible set of rules and heuristics that govern the morphology of the hydrothermally grown ZnO. In addition, we also employed supervised and unsupervised machine-learning models using conventional decision trees and classification and regression trees, respectively, to identify the dependence of the PCE of ZnO DSSCs on the different synthesis parameters. The reported work also evidences the PCE predictions of the ZnO DSSCs by using random forest and artificial neural network algorithms. The results substantiate that the random forest and artificial neural network algorithms successfully predict the PCE of the ZnO DSSCs with reasonable accuracy. Thus, we present a novel approach of applying statistical analysis and machine-learning algorithms to understand, discover, and predict the performance of DSSCs. We recommend extending the said know-how to other solar cells to identify rules and heuristics and experimentally realize highly efficient solar cells in shrinking manufacturing windows with a cost-effective approach.

Work Function-Tunable Amorphous Carbon-Silver Nanocomposite Hybrid Electrode for Optoelectronic Applications.

Parameters such as electrode work function (WF), optical reflectance, electrode morphology, and interface roughness play a crucial role in optoelectronic device design; therefore, fine-tuning these parameters is essential for efficient end-user applications. In this study, amorphous carbon-silver (C-Ag) nanocomposite hybrid electrodes are proposed and fully characterized for solar photovoltaic applications. Basically, the WF, sheet resistance, and optical reflectance of the C-Ag nanocomposite electrode are fine-tuned by varying the composition in a wide range. Experimental results suggest that irrespective of the variation in the graphite-silver composition, smaller and consistent grain size distributions offer uniform WF across the electrode surface. In addition, the strong C-Ag interaction in the nanocomposite enhances the nanomechanical properties of the hybrid electrode, such as hardness, reduced modulus, and elastic recovery parameters. Furthermore, the C-Ag nanocomposite hybrid electrode exhibits relatively lower surface roughness than the commercially available carbon paste electrode. These results suggest that the C-Ag nanocomposite electrode can be used for highly efficient photovoltaics in place of the conventional carbon-based electrodes.