Skyrmion-mediated nonvolatile ternary memory.

Multistate memory systems have the ability to store and process more data in the same physical space as binary memory systems, making them a potential alternative to existing binary memory systems. In the past, it has been demonstrated that voltage-controlled magnetic anisotropy (VCMA) based writing is highly energy-efficient compared to other writing methods used in non-volatile nano-magnetic binary memory systems. In this study, we introduce a new, VCMA-based and skyrmion-mediated non-volatile ternary memory system using a perpendicular magnetic tunnel junction (p-MTJ) in the presence of room temperature thermal perturbation. We have also shown that ternary states {- 1, 0, + 1} can be implemented with three magnetoresistance values obtained from a p-MTJ corresponding to ferromagnetic up, down, and skyrmion state, with 99% switching probability in the presence of room temperature thermal noise in an energy-efficient way, requiring ~ 2 fJ energy on an average for each switching operation. Additionally, we show that our proposed ternary memory demonstrates an improvement in area and energy by at least 2X and ~ 104X respectively, compared to state-of-the-art spin-transfer torque (STT)-based non-volatile magnetic multistate memories. Furthermore, these three states can be potentially utilized for energy-efficient, high-density in-memory quantized deep neural network implementation.

A Kernelized Classification Approach for Cancer Recognition Using Markovian Analysis of DNA Structure Patterns as Feature Mining.

Nucleotide-based molecules called DNA and RNA are essential for several biological processes that affect both normal and cancerous cells. They contain the critical genetic material needed for normal cell growth and functioning. The DNA structure patterns that make up the genetic code affect cells' growth, behavior, and control. Different DNA structure patterns indicate different physiological effects in the cell. Knowledge of these patterns is necessary to identify the molecular origins of cancer and other disorders. Analyzing these patterns can help in the early detection of diseases, which is essential for the effectiveness of cancer research and therapy. The novelty of this study is to examine the patterns of dinucleotide structure in many genomic regions, including the non-coding region sequence (N-CDS), coding region sequence (CDS), and whole raw DNA sequence (W.R. sequence). It provides an in-depth discussion of dinucleotide patterns related to these diverse genetic environments and contains malignant and non-malignant DNA sequences. The Markovian modeling that predicts dinucleotide probabilities also reduces feature complexity and minimizes computational costs compared to the approaches of Kernelized Logistic Regression (KLR) and Support Vector Machine (SVM). This technique is effectively evaluated in essential case studies, as indicated by accuracy metrics and 10-fold cross-validation. The classifier and feature reduction, which are generated by Markovian probability, operate well together and can help predict cancer. Our findings successfully distinguish DNA sequences related to cancer from those diagnostics of non-cancerous diseases by analyzing the W.R. DNA sequence as well as its CDS and N-CDS regions.

Co-pyrolysis of plastic waste and eucalyptus waste wood for fuel pellets production: A study of fuel, mechanical, and combustion characteristics under varying binder proportion.

The study explores the effect of varying molasses proportions as a binder on the characteristics of densified char obtained through the slow co-pyrolysis of plastic waste and Eucalyptus wood waste (Waste low-density polyethylene - Eucalyptus wood (WLDPE-EW) and Waste Polystyrene - Eucalyptus wood (WPS-EW)). Pyrolysis was conducted at 500 °C with a residence time of 120 min, employing plastic to wood waste ratios of 1:2 and 1:3 (w/w). The focus was on how varying the proportion of molasses (10-30 %), influences the physical and combustion properties of the resulting biofuel pellets. Our findings reveal that the calorific value of the pellets decreased from 28.94 to 27.44 MJ/Kg as the molasses content increased. However, this decrease in calorific value was compensated by an increase in pellet mass density, which led to a higher energy density overall. This phenomenon was attributed to the formation of solid bridges between particles, facilitated by molasses, effectively decreasing particle spacing. The structural integrity of the pellets, as measured by the impact resistance index, improved significantly (43-47 %) with the addition of molasses. However, a significant change in the combustion characteristics depicted by lower ignition and burnout temperatures were observed due to decrease in fixed carbon value and increase in volatile matter content, as the proportion of molasses increased. Despite these changes, the pellets demonstrated a stable combustion profile, suggesting that molasses are an effective binder for producing biofuel pellets through the densification of char derived from the co-pyrolysis of plastic and Eucalyptus wood waste. The optimized molasses concentration analyzed through multifactor regression analysis was 16.96 % with 28 % WLDPE proportion to produce WLDPE-EW char pellets. This study highlights the potential of using molasses as a sustainable binder to enhance the mechanical and combustion properties of biofuel pellets, offering a viable pathway for the valorization of waste materials.

A Comprehensive Fuzzy Model for Understanding Neuronal Calcium Distribution in Presence of VGCC, Na+/Ca2+ Exchanger, Buffer, and ER Fluxes.

Free Calcium ions in the cytosol are essential for many physiological and physical functions. The free calcium ions are commonly regarded as a second messenger, are an essential part of brain communication. Numerous physiological activities, such as calcium buffering and calcium ion channel flow, etc. influence the cytosolic calcium concentration. In light of the above, the primary goal of this study is to develop a model of calcium distribution in neuron cells when a Voltage-Gated Calcium Channel and Sodium Calcium Exchanger are present. As we know, decreased buffer levels and increased calcium activity in the Voltage-Gated Calcium Channel and Sodium Calcium Exchanger lead to Alzheimer's disease. Due to these changes, the calcium diffusion in that location becomes disrupted and impacted by Alzheimer's disease. The model has been constructed by considering key factors like buffers and ER fluxes when Voltage-Gated Calcium Channels and Sodium Calcium Exchangers are present. Based on the physiological conditions of the parameters, appropriate boundary conditions have been constructed in the fuzzy environment. This model is considered a fuzzy boundary value problem with the source term and initial boundary conditions are modeled by triangular fuzzy functions. In this, paper we observed the approximate solution of the mathematical model which was investigated by the fuzzy undetermined coefficient method. The solution has been performed through MATLAB and numerical results have been computed using simulation. The observation made that the proper operation of the Voltage-Gated Calcium Channel and Sodium Calcium Exchanger is critical for maintaining the delicate equilibrium of calcium ions, which regulates vital cellular activities. Dysregulation of Voltage-Gated Calcium Channel and Sodium Calcium Exchanger activity has been linked to neurodegenerative illnesses like Alzheimer's disease.

Exploring the potential of machine learning in gynecological care: a review.

Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. This paper investigates how machine learning (ML) algorithms are employed in the field of gynecology to tackle crucial issues pertaining to women's health. This paper also investigates the integration of ultrasound technology with artificial intelligence (AI) during the initial, intermediate, and final stages of pregnancy. Additionally, it delves into the diverse applications of AI throughout each trimester.This review paper provides an overview of machine learning (ML) models, introduces natural language processing (NLP) concepts, including ChatGPT, and discusses the clinical applications of artificial intelligence (AI) in gynecology. Additionally, the paper outlines the challenges in utilizing machine learning within the field of gynecology.

A spin wave driven skyrmion-based diode on a T-shaped nanotrack.

The propagation of spin waves is one of the promising ways to design nanoscale spintronic devices. The spin waves can interact with the magnetic skyrmion, a particle-like object that is topologically stabilized by Dzyaloshinskii-Moriya interaction (DMI) in thin film heterostructures. In this work, a spin wave-driven skyrmion-based diode is proposed by employing a T-shaped ferromagnetic nanotrack. The one-way motion of the skyrmion is achieved by exploiting the mid-arm at the center of the nanotrack. This prevents the reverse motion of the skyrmion owing to the skyrmion Hall effect (SkHE) and the absence of a repulsive force from the far edge in the mid-arm region. In order to facilitate the diode functionality of the spin wave-driven skyrmion, the amplitude and frequency of the excitation field should be considered in the ranges 0.07 T ≤ H0 ≤ 0.4 T and 60 GHz ≤ f ≤ 80 GHz, respectively. The micromagnetic interaction energy between the edges and the spin wave-driven skyrmion creates a potential gradient that induces the force which is responsible for the longitudinal motion of the skyrmion. The suggested spin wave driven diode exhibits a processing speed on the order of 100 m s-1 at 60 GHz frequency and 0.4 T amplitude. Hence, this device paves the way for the development of complete non-charge based magnetic devices for various spintronic applications.

PDA-Fe3O4 decorated carbon felt anode enhancing electrochemical performance of microbial fuel cells: Effect of electrode materials on electroactive biofilm.

Anode modification is an effective strategy for enhancing the electrochemical performance of microbial fuel cell (MFC). However, the impacts of the modified materials on anode biofilm development during MFC operation have been less studied. We prepared a novel PDA-Fe3O4-CF composite anode by coating original carbon felt anode (CF) with polydopamine (PDA) and Fe3O4 nanoparticles. The composite anode material was characterized by excellent hydrophilicity and electrical conductivity, and the anodic biofilm exhibited fast start-up, higher biomass, and more uniform biofilm layer after MFC operation. The MFC reactor assembled with the composite anode achieved a maximum power density of 608 mW m-2 and an output voltage of 586 mV, which were 316.4% and 72.4% higher than the MFC with the original CF anode, respectively. Microbial community analysis indicated that the modified anode biofilm had a higher relative abundance of exoelectrogen species in comparison to the unmodified anode. The PICRUSt data revealed that the anodic materials may affect the bioelectrochemical performance of the biofilm by influencing the expression levels of key enzyme genes involved in biofilm extracellular polymer (EPS) secretion and extracellular electron transfer (EET). The growth of the anodic biofilm would exert positive or negative influences on the efficiency of electricity production and electron transfer of the MFCs at different operating stages. This work expands the knowledge of the role that anodic materials play in the development and electrochemical performance of anodic biofilm in MFCs.

Quantized Magnetic Domain Wall Synapse for Efficient Deep Neural Networks.

The quantization of synaptic weights using emerging nonvolatile memory (NVM) devices has emerged as a promising solution to implement computationally efficient neural networks on resource constrained hardware. However, the practical implementation of such synaptic weights is hampered by the imperfect memory characteristics, specifically the availability of limited number of quantized states and the presence of large intrinsic device variation and stochasticity involved in writing the synaptic states. This article presents on-chip training and inference of a neural network using quantized magnetic domain wall (DW)-based synaptic array and CMOS peripheral circuits. A rigorous model of the magnetic DW device considering stochasticity and process variations has been utilized for the synapse. To achieve stable quantized weights, DW pinning has been achieved by means of physical constrictions. Finally, VGG8 architecture for CIFAR-10 image classification has been simulated by using the extracted synaptic device characteristics. The performance in terms of accuracy, energy, latency, and area consumption has been evaluated while considering the process variations and nonidealities in the DW device as well as the peripheral circuits. The proposed quantized neural network (QNN) architecture achieves efficient on-chip learning with 92.4% and 90.4% training and inference accuracy, respectively. In comparison to pure CMOS-based design, it demonstrates an overall improvement in area, energy, and latency by 13.8 × , 9.6 × , and 3.5 × , respectively.

From slag to green: Aided-phytoremediation as a sustainable tool to rehabilitate land contaminated by steel slag and assessment of CO2 sequestration.

Steel slag (SS) has many applications, but its immediate reuse is not possible due to its inherent swelling potential and presence of toxic metals. Therefore, it can only be used after the aging process, which can be either natural or artificial. While few large-scale steel plants afford artificial aging, many small-scale ones opt for natural aging through stockpiling of SS. This results in an increase in soil pH to over 12, thus damaging the ecosystem and making it unviable for plant growth. This research focuses on the reclamation of land affected by SS through the formation of a Phyto-barrier using 22 native plant species aided by the application of a 2 % (v/v) solution of the organic amendment. Furthermore, the superior performance of plants belonging to the Fabaceae family was ascertained, while establishing Sesbania grandiflora as an able species for aided-phytoremediation due to its remarkable growth (≈ 10 ft tall and 33 cm in circumference) during the study period. The CO2 sequestered by the plantation showed that maximum sequestration has been done by Sesbania grandiflora (49.96 kg CO2 / tree/ year), and least by Azadirachta indica (0.35 kg CO2/tree/year). The overall CO2 sequestered by the plantation stood at 3.85 tons/year. A cost-benefit analysis of using aided-phytoremediation indicates an expense of 90 $ per year as the recurring expense, while carbon credits if monetized, would yield 154 $ to 308 $ as returns. The investigations of this study established a new approach to vegetation over SS-affected land, through native species and the application of organic amendment.

Progress and perspectives on carbon-based materials for adsorptive removal and photocatalytic degradation of perfluoroalkyl and polyfluoroalkyl substances (PFAS).

Per- and polyfluoroalkyl substances (PFAS) (also known as 'forever chemicals') have emerged as trace pollutants of global concern, attributing to their persistent and bio-accumulative nature, pervasive distribution, and adverse public health and environmental impacts. The unregulated discharge of PFAS into aquatic environments represents a prominent threat to the wellbeing of humans and marine biota, thereby exhorting unprecedented action to tackle PFAS contamination. Indeed, several noteworthy technologies intending to remove PFAS from environmental compartments have been intensively evaluated in recent years. Amongst them, adsorption and photocatalysis demonstrate remarkable ability to eliminate PFAS from different water matrices. In particular, carbon-based materials, because of their diverse structures and many exciting properties, offer bountiful opportunities as both adsorbent and photocatalyst, for the efficient abatement of PFAS. This review, therefore, presents a comprehensive summary of the diverse array of carbonaceous materials, including biochar, activated carbon, carbon nanotubes, and graphene, that can serve as ideal candidates in adsorptive and photocatalytic treatment of PFAS contaminated water. Specifically, the efficacy of carbon-mediated PFAS removal via adsorption and photocatalysis is summarised, together with a cognizance of the factors influencing the treatment efficiency. The review further highlights the neoteric development on the novel innovative approach 'concentrate and degrade' that integrates selective adsorption of trace concentrations of PFAS onto photoactive surface sites, with enhanced catalytic activity. This technique is way more energy efficient than conventional energy-intensive photocatalysis. Finally, the review speculates the cardinal challenges associated with the practical utility of carbon-based materials, including their scalability and economic feasibility, for eliminating exceptionally stable PFAS from water matrices.

Effective method for upcycling construction and demolition waste into concrete: A life cycle approach.

Different property enhancement techniques have already been established to support upcycling of construction and demolition waste as aggregate in concrete. However, the most suitable and sustainable method is still unknown. Quality improvement of recycled coarse aggregate (RCA) after any treatment method and its environmental impact is estimated using life cycle analysis (LCA). This article compares the environmental impacts of such treatment methods on RCA and aims to find out the most suitable method with minimum impacts. The functional unit of this study is considered the preparation of 1 tonne of treated aggregate (recycled), considering reduction in water absorption after the treatment. An LCA is carried out using the SimaPro software (https://simapro.com/) followed by ISO 14040/44 guidelines. Based on the LCA environmental profiles, thermal treatment is the highest emission contributing removal method followed by mechanical grinding. In strengthening of attached mortar methods, accelerated carbonation process is the major emission contributing method followed by a specific microbial treatment. Moreover, a sensitivity analysis was performed by varying the energy mix with a focus on renewable-based energy mix. The sensitivity analysis shows a shift on selection for the suitable treatment method and other possibilities considering renewable-based energy mix. A preliminary assessment and probable impact prediction could be conceptualized before the adoption of any treatment method on RCA for a particular location.

Analyzing fuzzy boundary value problems: a study on the influence of mitochondria and ER fluxes on calcium ions in neuron cells.

Cytosolic-free calcium ions play an important role in various physical and physiological processes. A vital component of neural signaling is the free calcium ion concentration often known as the second messenger. There are many parameters that effect the cytosolic free calcium concentration like buffer, voltage-gated ion channels, Endoplasmic reticulum, Mitochondria, etc. Mitochondria are small organelles located within the nervous system that are involved in processes within cells such as calcium homeostasis management, energy generation, response to stress, and cell demise pathways. In this work, a mathematical model with fuzzy boundary values has been developed to study the effect of Mitochondria and ER fluxes on free Calcium ions. The intended findings are displayed utilizing the physiological understanding that amyloid beta plaques and tangles of neurofibrillary fibers have been identified as the two main causes of AD. The key conclusion of the work is the investigation of [Formula: see text] for healthy cells and cells affected by Alzheimer's disease, which may aid in the study of such processes for computational scientists and medical practitioners. Also, it has been shown that when a unique solution is found for a specific precise problem, it also successfully deals with any underlying ambiguity within the problem by utilizing a technique based on the principles of linear transformation. Furthermore, the comparison between the analytical approach and the generalized hukuhara derivative approach is shown here, which illustrates the benefits of the analytical approach. The simulation is carried out in MATLAB.

A holistic methodology for mitigating awkward postural risks: Evidence from South Indian small-scale industries.

BACKGROUND: Small-scale industries (SSI) are the global economy's backbone since most industrial workers are connected. Most of these workers are contractual and temporary without appropriate training. Also, the SSI does not have a standard workplace with an appropriate layout and infrastructure, as they manage with minimum resources. Therefore, the work hazards, i.e., musculoskeletal disorders and fatigue, often go unnoticed as holistic postural risk methodology is still scarce for identifying the awkward postures in SSI. OBJECTIVE: The present study proposes a novel holistic methodology to track and mitigate awkward postural risks in human-physical activities in SSI. To determine the effectiveness of the proposed methodology, a case study is presented in the South Indian Pump industry, wherein a critical workstation with a complex ergonomic work environment is employed. METHODS: An ergonomic evaluation was conducted empirically and numerically in the workplaces using Digital Human Models. In numerical evaluation, three virtual workspaces have been created to redesign the identified crucial workstation, focusing on ergonomics and workflow. RESULTS: The results obtained from the case study are encouraging for to use of the novel methodology in SSI. The case study reports that the proposed design significantly reduced the REBA score and WISHA lifting index by 6 and 1.20, respectively, without significant investment. CONCLUSION: The proposed methodology could encourage research to identify awkward posture in SSI.

Ergonomic investigations on novel dynamic postural estimator using blaze pose and transfer learning.

The aim is to develop a computer-based assessment model for novel dynamic postural evaluation using RULA. The present study proposed a camera-based, three-dimensional (3D) dynamic human pose estimation model using 'BlazePose' with a data set of 50,000 action-level-based images. The model was investigated using the Deep Neural Network (DNN) and Transfer Learning (TL) approach. The model has been trained to evaluate the posture with high accuracy, precision, and recall for each output prediction class. The model can quickly analyse the ergonomics of dynamic posture online and offline with a promising accuracy of 94.12%. A novel dynamic postural estimator using blaze pose and transfer learning is proposed and assessed for accuracy. The model is subjected to a constant muscle loading factor and foot support score that could evaluate one person with good image clarity at a time.Practitioner summary: A detailed investigation of dynamic work postures is largely missing in the literature. Experimental analysis has been performed using transfer learning, BlazePose, and RULA action levels. An overall accuracy of 94.12% is achieved for dynamic postural assessment.

2D dynamic analysis of the disturbances in the calcium neuronal model and its implications in neurodegenerative disease.

Ca2+ signaling is an essential function of neurons to control synaptic activity, memory formation, fertilization, proliferation, etc. Protein and voltage-dependent calcium channels (VDCCs) maintain an adequate level of calcium concentration ([Ca2+]). An alteration in [Ca2+] leads to the death of the neurons that start the primary symptoms of the disease. The present study deals with cell memory-based mathematical modeling of Ca2+ that is characterized by the presence of protein and VDCC. We developed a two-dimensional Ca2+ neuronal model to study the spatiotemporal behavior of the Ca2+ profile. All principal parameters like buffer concentration, diffusion coefficient, VDCC fluxes, etc. are incorporated in this model. Apposite initial and boundary conditions are applied to the physiology of the problem. We obtained an approximate Ca2+ profile by the fractional integral transform method. The application of obtained results is performed to provide its implications to estimate the [Ca2+] in neurodegenerative disease. It is observed that the protein and VDCC provide a significant impact in the presence of cell memory. The memory of cells shrinks the Ca2+ flow from elevation and provides better results to estimated Ca2+ flow in the disease state.

Skyrmion motion under temperature gradient and application in logic devices.

Under the presence of temperature gradient (TG) on a nanotrack, it is necessary to investigate the skyrmion dynamics in various magnetic systems under the combined effect of forces due to magnonic spin transfer torque(µSTT),thermal STT (τSTT), entropic difference(dS),as well as thermal induced dipolar field (DF). Hence, in this work, the dynamics of skyrmions in ferromagnets (FM), synthetic antiferromagnets (SAF), and antiferromagnets (AFM) have been studied under different TGs and damping constants (αG). It is observed thatαGplays a major role in deciding the direction of skyrmion motion either towards the hotter or colder side in different magnetic structures. Later, FM skyrmion based logic device is proposed that consists of a cross-coupled nanotrack, where the skyrmions on horizontal and vertical nanotrack are controlled by exploiting TG and electrical STT (eSTT), respectively by taking the advantages of thermal induced skyrmion Hall effect (SkHE). The proposed device performs AND and OR logic functionalities simultaneously, when the applied current density is2×1011Am-2.Moreover, the proposed device is also able to exhibit the half adder functionality by tuning the applied current density to3×1011Am-2.The total energy consumption for AND and OR logic operation and half adder are 33.63 fJ and 25.06 fJ, respectively. This paves the way for the development of energy-efficient logic devices with ultra-high storage density.

Spin device-based image edge detection architecture for neuromorphic computing.

Artificial intelligence and deep learning today are utilized for several applications namely image processing, smart surveillance, edge computing, and so on. The hardware implementation of such applications has been a matter of concern due to huge area and energy requirements. The concept of computing in-memory and the use of non-volatile memory (NVM) devices have paved a path for resource-efficient hardware implementation. We propose a dual-level spin-orbit torque magnetic random-access memory (SOT-DLC MRAM) based crossbar array design for image edge detection. The presented in-memory edge detection algorithm framework provides spin-based crossbar designs that can intrinsically perform image edge detection in an energy-efficient manner. The simulation results are scaled down in energy consumption for data transfer by a factor of 8x for grayscale images with a comparatively smaller crossbar than an equivalent CMOS design. DLC SOT-MRAM outperforms CMOS-based hardware implementation in several key aspects, offering 1.53x greater area efficiency, 14.24x lower leakage power dissipation, and 3.63x improved energy efficiency. Additionally, when compared to conventional spin transfer torque (STT-MRAM and SOT-MRAM, SOT-DLC MRAM achieves higher energy efficiency with a 1.07x and 1.03x advantage, respectively. Further, we extended the image edge extraction framework to spiking domain where ant colony optimization (ACO) algorithm is implemented. The mathematical analysis is presented for mapping of conductance matrix of the crossbar during edge detection with an improved area and energy efficiency at hardware implementation. The pixel accuracy of edge-detected image from ACO is 4.9% and 3.72% higher than conventional Sobel and Canny based edge-detection.

Nurture outpaces nature: fostering with an attentive mother alters social dominance in a mouse model of stress sensitivity.

Maternal care is critical for epigenetic programming during postnatal brain development. Stress is recognized as a critical factor that may affect maternal behavior, yet owing to high heterogeneity in stress response, its impact varies among individuals. We aimed here to understand the connection between inborn stress vulnerability, maternal care, and early epigenetic programming using mouse populations that exhibit opposite poles of the behavioral spectrum (social dominance [Dom] and submissiveness [Sub]) and differential response to stress. In contrast to stress-resilient Dom dams, stress-vulnerable Sub dams exhibit significantly lower maternal attachment, serum oxytocin, and colonic Lactobacillus reuteri populations. Sub offspring showed a reduced hippocampal expression of key methylation genes at postnatal day (PND) 7 and a lack of developmentally-dependent increase in 5-methylcytosine (5-mC) at PND 21. In addition, Sub pups exhibit significant hypermethylation of gene promoters connected with glutamatergic synapses and behavioral responses. We were able to reverse the submissive endophenotype through cross-fostering Sub pups with Dom dams (Sub/D). Thus, Sub/D pups exhibited elevated hippocampal expression of DNMT3A at PND 7 and increased 5-mC levels at PND 21. Furthermore, adult Sub/D offspring exhibited increased sociability, social dominance, and hippocampal glutamate and monoamine levels resembling the neurochemical profile of Dom mice. We postulate that maternal inborn stress vulnerability governs epigenetic patterning sculpted by maternal care and intestinal microbiome diversity during early developmental stages and shapes the array of gene expression patterns that may dictate neuronal architecture with a long-lasting impact on stress sensitivity and the social behavior of offspring.

Synthesis and commercialization of bioplastics: Organic waste as a sustainable feedstock.

Substituting synthetic plastics with bioplastics, primarily due to their inherent biodegradable properties, represents a highly effective strategy to address the current global issue of plastic waste accumulation in the environment. Advances in bioplastic research have led to the development of materials with improved properties, enabling their use in a wide range of applications in major commercial sectors. Bioplastics are derived from various natural sources such as plants, animals, and microorganisms. Polyhydroxyalkanoate (PHA), a biopolymer synthesized by bacteria through microbial fermentation, exhibits physicochemical and mechanical characteristics comparable to those of synthetic plastics. In response to the growing demand for these environmentally friendly plastics, researchers are actively investigating various cleaner production methods, including modification or derivatization of existing molecules for enhanced properties and new-generation applications to expand their market share in the coming decades. By 2026, the commercial manufacturing capacity of bioplastics is projected to reach 7.6 million tonnes, with Europe currently holding a significant market share of 43.5 %. Bioplastics are predominantly utilized in the packaging industry, indicating a strong focus of their application in the sector. With the anticipated rise in bioplastic waste volume over the next few decades, it is crucial to comprehend their fate in various environments to evaluate the overall environmental impact. Ensuring their complete biodegradation involves optimizing waste management strategies and appropriate disposal within these facilities. Future research efforts should prioritize exploration of their end-of-life management and toxicity assessment of degradation products. These efforts are crucial to ensure the economic viability and environmental sustainability of bioplastics as alternatives to synthetic plastics.

Composite of graphitic carbon nitride and TiO2 as photo-electro-catalyst in microbial fuel cell.

The widespread application of surfactants and their subsequent discharge in the receiving water bodies is a very common issue in developing countries. In the present investigation, a composite of graphitic carbon nitride (GCN) and TiO2 was used as a photo-electro-catalyst in a microbial fuel cell (MFC)-based hybrid system for bio-electricity production and simultaneous pollutant removal (organic matter and sodium dodecyl sulphate, SDS). The GCN: TiO2 composite with a ratio of 70:30 (by wt. %) revealed a better electrochemical response; thus, it was used as a photo-electro-catalyst in MFC. Additionally, the photochemical characterization indicated a decrease in the band gap and charge recombination of GCN-TiO2 composite compared to standalone TiO2, which indicated a conducive effect of GCN addition. Further, on the actual use as a photo-electro-catalyst, the GCN-TiO2 catalysed MFC attained 58.2 ± 9.6% and 86.5 ± 7.1% of COD and SDS removal; while simultaneously harvesting a maximum power density of 1.07 W m-3, which was higher than standalone TiO2-catalysed MFC. The follow-up treatment in the charcoal bio-filter and photo-cathodic chamber of the hybrid system further improved the overall COD and SDS removal efficiency to 92.1 ± 2.7 and 95.6 ± 1.5%, respectively. The electro-catalytic performance of the GCN-TiO2 can be attributed to the presence of nitrogen-active species in the composite. The results of this investigation demonstrated a potential MFC-based hybrid system for the simultaneous secondary and tertiary treatment of municipal wastewater. Consequently, the outcome of this investigation indicates an innovative research direction in the field of photo-electro-catalyst, which can fit into the role of a photo-catalyst as well as an electro-catalyst.