Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions.

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

CO2 Photoreduction Activity Enhancement and Unexpected Observation of Carbon Monoxide Adsorbates on the Surface of TiO2 Nanotube Arrays Synthesized in Formamide Electrolytes.

TiO2 nanotube arrays grown through electrochemical anodization in a formamide-based electrolyte (TNTA-FA) exhibited a whole host of unusual properties compared to nanotubes grown in the conventional ethylene glycol-based electrolyte (TNTA-EG). TNTA-FA exhibited shorter phonon lifetimes, lower lattice strain, more visible light absorption, lower work function, and a highly unusual adsorbate structure consisting of physisorbed and chemisorbed CO along with linearly adsorbed CO2 and various monodentate and bidentate carbonate species. The observation of adsorbed CO in the dark is highly unusual and indicates spontaneous deoxygenation of CO2 on the surface of TNTA-FA. The significance of this finding is that the formation of CO2•- is no longer the rate-limiting bottleneck for the reduction of CO2 on TNTA-FA surfaces as it is for all TiO2 surfaces. TNTA-FA samples are strongly colored (inclusive of a fluorescent green color) and consist of rounded, vertically oriented hollow cylinders as opposed to the honeycomb-like morphology of TNTA-EG arranged in an approximate triangular lattice. The photocatalytic activity was tested through the CO2 photoreduction and dye degradation tests. Formamide-based nanotubes outperformed the EG-based nanotubes by almost 1.7 and 2 times, respectively, in CO2 reduction and dye degradation tests done on methylene blue, brilliant green, and rhodamine B dyes. These results are attributed to stronger surface band bending in TNTA-FA which facilitates more efficient separation of photogenerated electron-hole pairs.

Potassium channels in behavioral brain disorders. Molecular mechanisms and therapeutic potential: A narrative review.

Potassium channels (K+-channels) selectively control the passive flow of potassium ions across biological membranes and thereby also regulate membrane excitability. Genetic variants affecting many of the human K+-channels are well known causes of Mendelian disorders within cardiology, neurology, and endocrinology. K+-channels are also primary targets of many natural toxins from poisonous organisms and drugs used within cardiology and metabolism. As genetic tools are improving and larger clinical samples are being investigated, the spectrum of clinical phenotypes implicated in K+-channels dysfunction is rapidly expanding, notably within immunology, neurosciences, and metabolism. K+-channels that previously were considered to be expressed in only a few organs and to have discrete physiological functions, have recently been found in multiple tissues and with new, unexpected functions. The pleiotropic functions and patterns of expression of K+-channels may provide additional therapeutic opportunities, along with new emerging challenges from off-target effects. Here we review the functions and therapeutic potential of K+-channels, with an emphasis on the nervous system, roles in neuropsychiatric disorders and their involvement in other organ systems and diseases.

Probe sonication-assisted rapid synthesis of highly fluorescent sulfur quantum dots.

A new type of heavy-metal free single-element nanomaterial, called sulfur quantum dots (SQDs), has gained significant attention due to its advantages over traditional semiconductor QDs for several biomedical and optoelectronic applications. A straightforward and rapid synthesis approach for preparing highly fluorescent SQDs is needed to utilize this nanomaterial for technological applications. Until now, only a few synthesis approaches have been reported; however, these approaches are associated with long reaction times and low quantum yields (QY). Herein, we propose a novel optimized strategy to synthesize SQDs using a mix of probe sonication and heating, which reduces the reaction time usually needed from 125 h to a mere 15 min. The investigation employs cavitation and vibration effects of high energy acoustic waves to break down the bulk sulfur into nano-sized particles in the presence of highly alkaline medium and oleic acid. In contrast to previous reports, the obtained SQDs exhibited excellent aqueous solubility, desirable photostability, and a relatively high photoluminescence QY up to 10.4% without the need of any post-treatment. Additionally, the as-synthesized SQDs show excitation-dependent emission and excellent stability in different pH (2-12) and temperature (20 °C-80 °C) environments. Hence, this strategy opens a new pathway for rapid synthesis of SQDs and may facilitate the use of these materials for biomedical and optoelectronic applications.

Radial Nano-Heterojunctions Consisting of CdS Nanorods Wrapped by 2D CN:PDI Polymer with Deep HOMO for Photo-Oxidative Water Splitting, Dye Degradation and Alcohol Oxidation.

Solar energy harvesting using semiconductor photocatalysis offers an enticing solution to two of the biggest societal challenges, energy scarcity and environmental pollution. After decades of effort, no photocatalyst exists which can simultaneously meet the demand for excellent absorption, high quantum efficiency and photochemical resilience/durability. While CdS is an excellent photocatalyst for hydrogen evolution, pollutant degradation and organic synthesis, photocorrosion of CdS leads to the deactivation of the catalyst. Surface passivation of CdS with 2D graphitic carbon nitrides (CN) such as g-C3N4 and C3N5 has been shown to mitigate the photocorrosion problem but the poor oxidizing power of photogenerated holes in CN limits the utility of this approach for photooxidation reactions. We report the synthesis of exfoliated 2D nanosheets of a modified carbon nitride constituted of tris-s-triazine (C6N7) linked pyromellitic dianhydride polydiimide (CN:PDI) with a deep oxidative highest occupied molecular orbital (HOMO) position, which ensures sufficient oxidizing power for photogenerated holes in CN. The heterojunction formed by the wrapping of mono-/few layered CN:PDI on CdS nanorods (CdS/CN:PDI) was determined to be an excellent photocatalyst for oxidation reactions including photoelectrochemical water splitting, dye decolorization and the photocatalytic conversion of benzyl alcohol to benzaldehyde. Extensive structural characterization using HR-TEM, Raman, XPS, etc., confirmed wrapping of few-layered CN:PDI on CdS nanorods. The increased photoactivity in CdS/CN:PDI catalyst was ascribed to facile electron transfer from CdS to CN:PDI in comparison to CdS/g-C3N4, leading to an increased electron density on the surface of the photocatalyst to drive chemical reactions.

Enhanced luminescence sensing performance and increased intrachain order in blended films of P3HT and cellulose nanocrystals.

Blended films comprising poly(butyl acrylate) (PBA)-grafted cellulose nanocrystals (CNCs) and poly(3-hexylthiophene) (P3HT), exhibited more intense photoluminescence (PL) and longer PL emission lifetimes compared to pristine P3HT films. Optical absorption and photoluminescence spectra indicated reduced torsional disorder i.e. enhanced backbone planarity in the P3HT@CNC blended composites compared to the bare P3HT. Such molecule-level geometrical modification resulted in both smaller interchain and higher intrachain exciton bandwidth in the blended composites compared to the bare P3HT, because of reduced interchain interactions and enhanced intrachain order. These results indicate a potential switch of the aggregation behavior from dominant H-aggregates to J-aggregates, supported by Raman spectroscopy. The reorganization of micromolecular structure and concomitant macroscopic aggregation of the conjugated polymer chains resulted in a longer conjugation length for the P3HT@CNC blended composites compared to the bare P3HT. Additionally, this nanoscale morphological change produced a reduction in the highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) energy gap of the blends, evidenced from optical absorption spectra. Classical molecular dynamics simulation studies predicted the probability of enhanced planarity in the polymer backbone following interactions with CNC surfaces. Theoretical results from density functional theory calculations corroborate the experimentally observed reduction of optical bandgap in the blends compared to bare P3HT. The blended composite outperformed the bare P3HT in nitro-group PL sensing tests with a pronounced difference in the reaction kinetics. While the PL quenching dynamics for bare P3HT followed Stern-Volmer kinetics, the P3HT@CNC blended composite exhibited a drastic deviation from the same. This work shows the potential of a functionalized rod-like biopolymer in tuning the optoelectronic properties of a technologically important polymeric organic semiconductor through control of the nanoscale morphology.

Endogenous glutamine is rate-limiting for anti-CD3 and anti-CD28 induced CD4+ T-cell proliferation and glycolytic activity under hypoxia and normoxia.

To meet the demand for energy and biomass, T lymphocytes (T cells) activated to proliferation and clonal expansion, require uptake and metabolism of glucose (Gluc) and the amino acid (AA) glutamine (Gln). Whereas exogenous Gln is converted to glutamate (Glu) by glutaminase (GLS), Gln is also synthesized from the endogenous pool of AA through Glu and activity of glutamine synthase (GS). Most of this knowledge comes from studies on cell cultures under ambient oxygen conditions (normoxia, 21% O2). However, in vivo, antigen induced T-cell activation often occurs under moderately hypoxic (1-4% O2) conditions and at various levels of exogenous nutrients. Here, CD4+ T cells were stimulated for 72 h with antibodies targeting the CD3 and CD28 markers at normoxia and hypoxia (1% O2). This was done in the presence and absence of the GLS and GS inhibitors, Bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl) ethyl sulfide (BPTES) and methionine sulfoximine (MSO) and at various combinations of exogenous Gluc, Gln and pyruvate (Pyr) for the last 12 h of stimulation. We found that T-cell proliferation, viability and levels of endogenous AA were significantly influenced by the availability of exogenous Gln, Gluc and Pyr as well as inhibition of GLS and GS. Moreover, inhibition of GLS and GS and levels of oxygen differentially influenced oxygen consumption rate (OCR) and extracellular acidification rate (ECAR). Finally, BPTES-dependent down-regulation of ECAR was associated with reduced hexokinase (HK) activity at both normoxia and hypoxia. Our results demonstrate that Gln availability and metabolism is rate-limiting for CD4+ T-cell activity.

Deep Learning for Ocular Disease Recognition: An Inner-Class Balance.

It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated. Therefore, an automated ocular disease detection system with computer-aided tools is necessary to detect various eye disorders using fundus pictures. Such a system is now possible as a consequence of deep learning algorithms that have improved image classification capabilities. A deep-learning-based approach to targeted ocular detection is presented in this study. For this study, we used state-of-the-art image classification algorithms, such as VGG-19, to classify the ODIR dataset, which contains 5000 images of eight different classes of the fundus. These classes represent different ocular diseases. However, the dataset within these classes is highly unbalanced. To resolve this issue, the work suggested converting this multiclass classification problem into a binary classification problem and taking the same number of images for both classifications. Then, the binary classifications were trained with VGG-19. The accuracy of the VGG-19 model was 98.13% for the normal (N) versus pathological myopia (M) class; the model reached an accuracy of 94.03% for normal (N) versus cataract (C), and the model provided an accuracy of 90.94% for normal (N) versus glaucoma (G). All of the other models also improve the accuracy when the data is balanced.

Synergistic Enhancement of the Photoelectrochemical Performance of TiO2 Nanorod Arrays through Embedded Plasmon and Surface Carbon Nitride Co-sensitization.

We report a unique photoanode architecture involving TiO2, g-C3N4, and AuNPs wherein a synergistic enhancement of the photoelectrochemical (PEC) performance was obtained with photocurrent densities as high as 3 mA cm-2 under AM1.5G 1 sun illumination. The PEC performance was highly stable and reproducible, and a photoresponse was obtained down to a photon energy of 2.4 eV, close to the interband damping threshold of Au. The photocurrent enhancement was maximized when the Au plasmon band strongly overlapped the g-C3N4 emission band. Our photoanode architecture, which involved AuNPs buried under TiO2 and a plasmon-induced resonance energy transfer-like interaction between g-C3N4 quantum dots (CNQDs) and AuNPs, solved four major problems associated with plasmonic photoelectrocatalysis─it reduced recombination by limiting eliminating direct electrolyte access to AuNPs, it facilitated electron extraction through single-crystal TiO2 nanorod percolation pathways, it facilitated hole extraction through a defective TiO2 seed layer or canopy, and it expanded the range of visible light harvesting by pumping the Au surface plasmons from CNQDs through exciton-to-plasmon resonant energy transfer.

Knowledge distillation approach towards melanoma detection.

Melanoma is regarded as the most threatening among all skin cancers. There is a pressing need to build systems which can aid in the early detection of melanoma and enable timely treatment to patients. Recent methods are geared towards machine learning based systems where the task is posed as image recognition, tag dermoscopic images of skin lesions as melanoma or non-melanoma. Even though these methods show promising results in terms of accuracy, they are computationally quite expensive to train, that questions the ability of these models to be deployable in a clinical setting or memory constraint devices. To address this issue, we focus on building simple and performant models having few layers, less than ten compared to hundreds. As well as with fewer learnable parameters, 0.26 million (M) compared to 42.5 M using knowledge distillation with the goal to detect melanoma from dermoscopic images. First, we train a teacher model using a ResNet-50 to detect melanoma. Using the teacher model, we train the student model known as Distilled Student Network (DSNet) which has around 0.26 M parameters using knowledge distillation achieving an accuracy of 91.7%. We compare against ImageNet pre-trained models such MobileNet, VGG-16, Inception-V3, EfficientNet-B0, ResNet-50 and ResNet-101. We find that our approach works well in terms of inference runtime compared to other pre-trained models, 2.57 s compared to 14.55 s. We find that DSNet (0.26 M parameters), which is 15 times smaller, consistently performs better than EfficientNet-B0 (4 M parameters) in both melanoma and non-melanoma detection across Precision, Recall and F1 scores.

IoT-Based Water Quality Assessment System for Industrial Waste WaterHealthcare Perspective.

The environment, especially water, gets polluted due to industrialization and urbanization. Pollution due to industrialization and urbanization has harmful effects on both the environment and the lives on Earth. This polluted water can cause food poisoning, diarrhea, short-term gastrointestinal problems, respiratory diseases, skin problems, and other serious health complications. In a developing country like Bangladesh, where ready-made garments sector is one of the major sources of the total Gross Domestic Product (GDP), most of the wastes released from the garment factories are dumped into the nearest rivers or canals. Hence, the quality of the water of these bodies become very incompatible for the living beings, and so, it has become one of the major threats to the environment and human health. In addition, the amount of fish in the rivers and canals in Bangladesh is decreasing day by day as a result of water pollution. Therefore, to save fish and other water animals and the environment, we need to monitor the quality of the water and find out the reasons for the pollution. Real-time monitoring of the quality of water is vital for controlling water pollution. Most of the approaches for controlling water pollution are mainly biological and lab-based, which takes a lot of time and resources. To address this issue, we developed an Internet of Things (IoT)-based real-time water quality monitoring system, integrated with a mobile application. The proposed system in this research measures some of the most important indexes of water, including the potential of hydrogen (pH), total dissolved solids (TDS), and turbidity, and temperature of water. The proposed system results will be very helpful in saving the environment, and thus, improving the health of living creatures on Earth.

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data.

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people's feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people's minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public's opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

Development of an IoT-Based Sleep Apnea Monitoring System for Healthcare Applications.

Sleep is an essential and vital element of a person's life and health that helps to refresh and recharge the mind and body of a person. The quality of sleep is very important in every person's lifestyle, removing various diseases. Bad sleep is a big problem for a lot of people for a very long time. People suffering from various diseases are dealing with various sleeping disorders, commonly known as sleep apnea. A lot of people die during sleep because of uneven body changes in the body during sleep. On that note, a system to monitor sleep is very important. Most of the previous systems to monitor sleeping problems cannot deal with the real time sleeping problem, generating data after a certain period of sleep. Real-time monitoring of sleep is the key to detecting sleep apnea. To solve this problem, an Internet of Things- (IoT-) based real-time sleep apnea monitoring system has been developed. It will allow the user to measure different indexes of sleep and will notify them through a mobile application when anything odd occurs. The system contains various sensors to measure the electrocardiogram (ECG), heart rate, pulse rate, skin response, and SpO2 of any person during the entire sleeping period. This research is very useful as it can measure the indexes of sleep without disturbing the person and can also show it in the mobile application simultaneously with the help of a Bluetooth module. The system has been developed in such a way that it can be used by every kind of person. Multiple analog sensors are used with the Arduino UNO to measure different parameters of the sleep factor. The system was examined and tested on different people's bodies. To analyze and detect sleep apnea in real-time, the system monitors several people during the sleeping period. The results are displayed on the monitor of the Arduino boards and in the mobile application. The analysis of the achieved data can detect sleep apnea in some of the people that the system monitored, and it can also display the reason why sleep apnea happens. This research also analyzes the people who are not in the danger of sleeping problems by the achieved data. This paper will help everyone learn about sleep apnea and will help people detect it and take the necessary steps to prevent it.

Zinc phthalocyanine conjugated cellulose nanocrystals for memory device applications.

We present the electrical properties of zinc phthalocyanine covalently conjugated to cellulose nanocrystals (CNC@ZnPc). Thin films of CNC@ZnPc sandwiched between two gold electrodes showed pronounced hysteresis in their current-voltage characteristics. The layered metal-organic-metal sandwich devices exhibit distinct high and low conductive states when bias is applied, which can be used to store information. Density functional theory results confirmed wave function overlap between CNC and ZnPc in CNC@ZnPc, and helped visualize the lowest (lowest unoccupied molecular orbital) and highest molecular orbitals (highest occupied molecular orbital) in CNC@ZnPc. These results pave the way forward for all-organic electronic devices based on low cost, earth abundant CNCs and metallophthalocyanines.

Photocatalytic Mechanism Control and Study of Carrier Dynamics in CdS@C3N5 Core-Shell Nanowires.

We present a potential solution to the problem of extraction of photogenerated holes from CdS nanocrystals and nanowires. The nanosheet form of C3N5 is a low-band-gap (Eg = 2.03 eV), azo-linked graphenic carbon nitride framework formed by the polymerization of melem hydrazine (MHP). C3N5 nanosheets were either wrapped around CdS nanorods (NRs) following the synthesis of pristine chalcogenide or intercalated among them by an in situ synthesis protocol to form two kinds of heterostructures, CdS-MHP and CdS-MHPINS, respectively. CdS-MHP improved the photocatalytic degradation rate of 4-nitrophenol by nearly an order of magnitude in comparison to bare CdS NRs. CdS-MHP also enhanced the sunlight-driven photocatalytic activity of bare CdS NWs for the decolorization of rhodamine B (RhB) by a remarkable 300% through the improved extraction and utilization of photogenerated holes due to surface passivation. More interestingly, CdS-MHP provided reaction pathway control over RhB degradation. In the absence of scavengers, CdS-MHP degraded RhB through the N-deethylation pathway. When either hole scavenger or electron scavenger was added to the RhB solution, the photocatalytic activity of CdS-MHP remained mostly unchanged, while the degradation mechanism shifted to the chromophore cleavage (cycloreversion) pathway. We investigated the optoelectronic properties of CdS-C3N5 heterojunctions using density functional theory (DFT) simulations, finite difference time domain (FDTD) simulations, time-resolved terahertz spectroscopy (TRTS), and photoconductivity measurements. TRTS indicated high carrier mobilities >450 cm2 V-1 s-1 and carrier relaxation times >60 ps for CdS-MHP, while CdS-MHPINS exhibited much lower mobilities <150 cm2 V-1 s-1 and short carrier relaxation times <20 ps. Hysteresis in the photoconductive J-V characteristics of CdS NWs disappeared in CdS-MHP, confirming surface passivation. Dispersion-corrected DFT simulations indicated a delocalized HOMO and a LUMO localized on C3N5 in CdS-MHP. C3N5, with its extended π-conjugation and low band gap, can function as a shuttle to extract carriers and excitons in nanostructured heterojunctions, and enhance performance in optoelectronic devices. Our results demonstrate how carrier dynamics in core-shell heterostructures can be manipulated to achieve control over the reaction mechanism in photocatalysis.

Harvesting Hot Holes in Plasmon-Coupled Ultrathin Photoanodes for High-Performance Photoelectrochemical Water Splitting.

The harvesting of hot carriers produced by plasmon decay to generate electricity or drive a chemical reaction enables the reduction of the thermalization losses associated with supra-band gap photons in semiconductor photoelectrochemical (PEC) cells. Through the broadband harvesting of light, hot-carrier PEC devices also produce a sensitizing effect in heterojunctions with wide-band gap metal oxide semiconductors possessing good photostability and catalytic activity but poor absorption of visible wavelength photons. There are several reports of hot electrons in Au injected over the Schottky barrier into crystalline TiO2 and subsequently utilized to drive a chemical reaction but very few reports of hot hole harvesting. In this work, we demonstrate the efficient harvesting of hot holes in Au nanoparticles (Au NPs) covered with a thin layer of amorphous TiO2 (a-TiO2). Under AM1.5G 1 sun illumination, photoanodes consisting of a single layer of ∼50 nm diameter Au NPs coated with a 10 nm shell of a-TiO2 (Au@a-TiO2) generated 2.5 mA cm-2 of photocurrent in 1 M KOH under 0.6 V external bias, rising to 3.7 mA cm-2 in the presence of a hole scavenger (methanol). The quantum yield for hot-carrier-mediated photocurrent generation was estimated to be close to unity for high-energy photons (λ < 420 nm). Au@a-TiO2 photoelectrodes produced a small positive photocurrent of 0.1 mA cm-2 even at a bias of -0.6 V indicating extraction of hot holes even at a strong negative bias. These results together with density functional theory modeling and scanning Kelvin probe force microscope data indicate fast injection of hot holes from Au NPs into a-TiO2 and light harvesting performed near-exclusively by Au NPs. For comparison, Au NPs coated with a 10 nm shell of Al2O3 (Au@Al2O3) generated 0.02 mA cm-2 of photocurrent in 1 M KOH under 0.6 V external bias. These results underscore the critical role played by a-TiO2 in the extraction of holes in Au@a-TiO2 photoanodes, which is not replicated by an ordinary dielectric shell. It is also demonstrated here that an ultrathin photoanode (<100 nm in maximum thickness) can efficiently drive sunlight-driven water splitting.

Synthesis, characterization, and visible light photocatalytic activity of solution-processed free-standing 2D Bi2O2Se nanosheets.

Owing to their unique structural and electronic properties such as layered structure with tuneable bandgap and high electron mobility, 2D materials have emerged as promising candidates for photocatalysis. Recently, bismuth oxyselenide (Bi2O2Se), a member of bismuth oxychalcogenide's family has shown great potential in high-speed field-effect transistors, infrared photodetectors, ferroelectric devices, and electrochemical sensors. However, the potential of Bi2O2Se in photocatalysis has not yet been explored. In the current work, Bi2O2Se nanosheets with an average size of ∼170 nm and a lattice strain of 0.01 were synthesized at room temperature using a facile solution-processed method and the as-synthesized material was investigated with various characterization techniques such as x-ray diffraction, FE-SEM, UV-vis spectroscopy. The bandgap for the indirect transition in Bi2O2Se nanosheets was estimated to be 1.19 eV. Further, the visible-light-driven photocatalytic degradation of methylene blue (MB) dye using Bi2O2Se as a photocatalyst is presented. The photocatalytic experiments demonstrate the promising photocatalytic ability of Bi2O2Se as it leads to 25.06% degradation of MB within 80 min of light illumination. The effect of active species trapping agents (carrier and radical scavengers) on photocatalytic activity is also presented and discussed.

Nonmonotonic confining potential and eigenvalue density transition for generalized random matrix model.

We consider several limiting cases of the joint probability distribution for a random matrix ensemble with an additional interaction term controlled by an exponent γ (called the γ ensembles). The effective potential, which is essentially the single-particle confining potential for an equivalent ensemble with γ=1 (called the Muttalib-Borodin ensemble), is a crucial quantity defined in solution to the Riemann-Hilbert problem associated with the γ ensembles. It enables us to numerically compute the eigenvalue density of γ ensembles for all γ>0. We show that one important effect of the two-particle interaction parameter γ is to generate or enhance the nonmonotonicity in the effective single-particle potential. For suitable choices of the initial single-particle potentials, reducing γ can lead to a large nonmonotonicity in the effective potential, which in turn leads to significant changes in the density of eigenvalues. For a disordered conductor, this corresponds to a systematic decrease in the conductance with increasing disorder. This suggests that appropriate models of γ ensembles can be used as a possible framework to study the effects of disorder on the distribution of conductances.

Water-splitting photoelectrodes consisting of heterojunctions of carbon nitride with ap-type low bandgap double perovskite oxide.

Quinary and senary non-stoichiometric double perovskites such as Ba2Ca0.66Nb1.34-xFexO6-δ(BCNF) have been utilized for gas sensing, solid oxide fuel cells and thermochemical CO2reduction. Herein, we examined their potential as narrow bandgap semiconductors for use in solar energy harvesting. A cobalt co-doped BCNF, Ba2Ca0.66Nb0.68Fe0.33Co0.33O6-δ(BCNFCo), exhibited an optical absorption edge at ∼800 nm,p-type conduction and a distinct photoresponse up to 640 nm while demonstrating high thermochemical stability. A nanocomposite of BCNFCo and g-C3N4(CN) was prepared via a facile solvent-assisted exfoliation/blending approach using dichlorobenzene and glycerol at a moderate temperature. The exfoliation of g-C3N4followed by wrapping on perovskite established an effective heterojunction between the materials for charge separation. The conjugated 2D sheets of CN enabled better charge migration resulting in increased photoelectrochemical performance. A blend composed of 40 wt% perovskites and CN performed optimally, whilst achieving a photocurrent density as high as 1.5 mA cm-2for sunlight-driven water-splitting with a Faradaic efficiency as high as ∼88%.

Asymmetric Multipole Plasmon-Mediated Catalysis Shifts the Product Selectivity of CO2 Photoreduction toward C2+ Products.

Cu/TiO2 is a well-known photocatalyst for the photocatalytic transformation of CO2 into methane. The formation of C2+ products such as ethane and ethanol rather than methane is more interesting due to their higher energy density and economic value, but the formation of C-C bonds is currently a major challenge in CO2 photoreduction. In this context, we report the dominant formation of a C2 product, namely, ethane, from the gas-phase photoreduction of CO2 using TiO2 nanotube arrays (TNTAs) decorated with large-sized (80-200 nm) Ag and Cu nanoparticles without the use of a sacrificial agent or hole scavenger. Isotope-labeled mass spectrometry was used to verify the origin and identity of the reaction products. Under 2 h AM1.5G 1-sun illumination, the total rate of hydrocarbon production (methane + ethane) was highest for AgCu-TNTA with a total CxH2x+2 rate of 23.88 µmol g-1 h-1. Under identical conditions, the CxH2x+2 production rates for Ag-TNTA and Cu-TNTA were 6.54 and 1.39 µmol g-1 h-1, respectively. The ethane selectivity was the highest for AgCu-TNTA with 60.7%, while the ethane selectivity was found to be 15.9 and 10% for the Ag-TNTA and Cu-TNTA, respectively. Adjacent adsorption sites in our photocatalyst develop an asymmetric charge distribution due to quadrupole resonances in large metal nanoparticles and multipole resonances in Ag-Cu heterodimers. Such an asymmetric charge distribution decreases adsorbate-adsorbate repulsion and facilitates C-C coupling of reaction intermediates, which otherwise occurs poorly in TNTAs decorated with small metal nanoparticles.