Does the doping strategy of ferrite nanoparticles create a correlation between reactivity and toxicity?

Owing to their remarkable properties in terms of electrical resistivity, chemical stability, and saturation magnetisation, ferrite nanoparticles are being increasingly used for a wide range of applications. This study looks to investigate as to whether ferrite nanoparticles can be safely and viably doped with transition metal elements without adversely affecting the stability and toxicity of the nanoparticles. Monodispersed and phase pure variants of ferrites (MxFe3−xO4 where M = Co, Cu, Zn, Mn) were synthesised with a size range of 9-11 nm using a wet chemistry route. The doping % within the ferrites was within the range of 15-18% for all the dopants. Compared to ferrite nanoparticles, Co and Mn doping significantly enhanced the dissolution, whereas doping with Cu and Zn had an opposite effect to dissolution. DFT calculations performed on the ferrites to calculate the vacancy formation energy of Fe and dopant atoms substantiated the experimental dissolution data. A549 cells showed a dose dependent response (10-200 μg mL−1) and the reduction in cell viability followed the trend of MnxFe3−xO4 &gt; CoxFe3−xO4 &gt; ZnxFe3−xO4 &gt; CuxFe3−xO4 &gt; Fe3O4. A correlation study between dissolution, cell viability and uptake indicated cell viability and dissolution had a strong negative correlation for Fe3O4, and CoxFe3−xO4 whereas for CuxFe3−xO4 this correlation was very weak. We conclude by providing an overview of the impact of doping on the safety of other metal-oxide nanoparticles (CuO, ZnO, TiO2 and CeO2) in comparison to ferrite nanoparticles.</p

Probabilistic Photonic Computing with Chaotic Light

Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data, a task that poses significant challenges to traditional processors. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as "point estimates" that do not capture the uncertainty of prediction, which is an inherently probabilistic process. In contrast, treating an ANN as a probabilistic model derived via Bayesian inference poses significant challenges for conventional deterministic computing architectures. Here, we use chaotic light in combination with incoherent photonic data processing to enable high-speed probabilistic computation and uncertainty quantification. Since both the chaotic light source and the photonic crossbar support multiple independent computational wavelength channels, we sample from the output distributions in parallel at a sampling rate of 70.4 GS/s, limited only by the electronic interface. We exploit the photonic probabilistic architecture to simultaneously perform image classification and uncertainty prediction via a Bayesian neural network. Our prototype demonstrates the seamless cointegration of a physical entropy source and a computational architecture that enables ultrafast probabilistic computation by parallel sampling

Partial coherence enhances parallelized photonic computing

Advancements in optical coherence control1–5 have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography6–8. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities9–11. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson’s disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically)

Does the doping strategy of ferrite nanoparticles create a correlation between reactivity and toxicity?

The doping strategy of ferrite nanoparticles induced a correlation between their reactivity and toxicity. The evidence showed the induction of biological responses as a factor of their dissolution and suspension properties of ferrite nanoparticles.</jats:p

Scalable non-volatile tuning of photonic computational memories by automated silicon ion implantation (Adv. Mater. 8/2024)

This is the final version. Available from Wiley via the DOI in this record. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.Photonic integrated circuits (PICs) are revolutionizing the realm of information technology, promising unprecedented speeds and efficiency in data processing and optical communication. However, the nanoscale precision required to fabricate these circuits at scale presents significant challenges, due to the need to maintain consistency across wavelength-selective components, which necessitates individualized adjustments after fabrication. Harnessing spectral alignment by automated silicon ion implantation, in this work scalable and non-volatile photonic computational memories are demonstrated in high-quality resonant devices. Precise spectral trimming of large-scale photonic ensembles from a few picometers to several nanometres is achieved with long-term stability and marginal loss penalty. Based on this approach, spectrally aligned photonic memory and computing systems for general matrix multiplication are demonstrated, enabling wavelength multiplexed integrated architectures at large scales.European Union’s Horizon 2020European Union’s Innovation Council Pathfinder programmeDeutsche Forschungsgemeinschaft (DFG, German Research Foundation)Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)Volkswagen FoundationEuropean Research Counci