Active meta-learning for predicting and selecting perovskite crystallization experiments.

Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations, there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. In this paper, we applied the model-agnostic meta-learning (MAML) model and the Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammonium lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models to predict reaction compositions that result in crystals. We then evaluated the best three algorithms (PLATIPUS and active-learning k-nearest neighbor and decision tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcomes compared to other active-learning algorithms and a random baseline.

Instability in CH3NH3PbI3 perovskite solar cells due to elemental migration and chemical composition changes.

Organic-inorganic halide perovskites have rapidly grown as favorable materials for photovoltaic applications, but accomplishing long-term stability is still a major research problem. This work demonstrates a new insight on instability and degradation factors in CH3NH3PbI3 perovskite solar cells aging with time in open air. X-ray photoelectron spectroscopy (XPS) has been used to investigate the compositional changes caused by device degradation over the period of 1000 hrs. XPS spectra confirm the migration of metallic ions from the bottom electrode (ITO) as a key factor causing the chemical composition change in the perovskite layer besides the diffusion of oxygen. XPS results are in good agreement with the crystallographic marks. Glow discharge optical emission spectrometry (GD-OES) has also been performed on the samples to correlate the XPS results. Based on the experimental results, fundamental features that account for the instability in the perovskite solar cell is discussed.

Compositional engineering of VOPcPhO-TiO2 nano-composite to reduce the absolute threshold value of humidity sensors.

This research work demonstrates compositional engineering of an organic-inorganic hybrid nano-composites for modifying absolute threshold of humidity sensors. Vanadyl-2,9,16,23-tetraphenoxy-29H,31H-phthalocyanine (VOPcPhO), an organic semiconductor, doped with Titanium-dioxide nanoparticles (TiO2 NPs) has been employed to fabricate humidity sensors. The morphology of the VOPcPhO:TiO2 nano-composite films has been analyzed by atomic force microscopy (AFM) and field emission scanning electron microscopy (FESEM). The sensors have been examined over a wide range of relative humidity i.e. 20-99% RH. The sensor with TiO2 (90nm) shows reduced sensitivity-threshold and improved linearity. The VOPcPhO:TiO2 (90nm) nano-composite film is comprised of uniformly distributed voids which makes the surface more favorable for adsorption of moisture content from environment. The VOPcPhO:TiO2 nano-composite based sensor demonstrates remarkable improvement in the sensing parameter when equated with VOPcPhO sensors.

A novel classification of prostate specific antigen (PSA) biosensors based on transducing elements.

During the last few decades, there has been a tremendous rise in the number of research studies dedicated towards the development of diagnostic tools based on bio-sensing technology for the early detection of various diseases like cardiovascular diseases (CVD), many types of cancer, diabetes mellitus (DM) and many infectious diseases. Many breakthroughs have been developed in the areas of improving specificity, selectivity and repeatability of the biosensor devices. Innovations in the interdisciplinary areas like biotechnology, genetics, organic electronics and nanotechnology also had a great positive impact on the growth of bio-sensing technology. As a product of these improvements, fast and consistent sensing policies have been productively created for precise and ultrasensitive biomarker-based disease diagnostics. Prostate-specific antigen (PSA) is widely considered as an important biomarker used for diagnosing prostate cancer. There have been many publications based on various biosensors used for PSA detection, but a limited review was available for the classification of these biosensors used for the detection of PSA. This review highlights the various biosensors used for PSA detection and proposes a novel classification for PSA biosensors based on the transducer type used. We also highlight the advantages, disadvantages and limitations of each technique used for PSA biosensing which will make this article a complete reference tool for the future researches in PSA biosensing.