THRESHOLD IONIZATION SPECTROSCOPY OF La(CH3CN) AND La(C4H9CN) RADICALS FORMED BY La REACTIONS WITH ALKANE NITRILES

La atom reactions with acetonitrile (CH$_{3}$CN) and pentanenitrile (C$_{4}$H$_{9}$CN) are carried out in a laser-vaporization supersonic molecular beam source. Metal-containing species are observed using time-of-flight mass spectrometry. In this talk, we report the mass-analyzed threshold ionization (MATI) spectroscopic characterization of two metal-containing radicals, La(CH$_{3}$CN) and La(C$_{4}$H$_{9}$CN), formed by La associations with acetonitrile and pentanenitrile, respectively. Adiabatic ionization energies of the two La-alkane nitrile species and their vibrational frequencies are measured from the MATI spectra. Metal-ligand binding modes and molecular structures are investigated by comparing the spectroscopic measurements with density functional theory calculations and spectral simulations. For both alkane nitriles, the preferred La binding site is identified to be the nitrile group with a $pi$-bind mode, the resultant metal complexes are three-membered metallacycles. While a single isomer is observed for La(CH$_{3}$CN), two rotational conformers are identified for La(C$_{4}$H$_{9}$CN). The binding and structures of these metal-alkane nitrile radicals are different from those formed by metal ion reactions, where metal ions were reported to favor $sigma$_x000d_ binding with the nitrogen atom.footnote{K. Eller, W. Zummack, H. Schwarz, L. M. Roth, B. S. Freiser, textit{J. Am. Chem. Soc.}, textbf{1991}, textit{113}, 833-839

Premade Nanoparticle Films for the Synthesis of Vertically Aligned Carbon Nanotubes

Carbon nanotubes (CNTs) offer unique properties that have the potential to address multiple issues in industry and material sciences. Although many synthesis methods have been developed, it remains difficult to control CNT characteristics. Here, with the goal of achieving such control, we report a bottom-up process for CNT synthesis in which monolayers of premade aluminum oxide (Al2O3) and iron oxide (Fe3O4) nanoparticles were anchored on a flat silicon oxide (SiO2) substrate. The nanoparticle dispersion and monolayer assembly of the oleic-acid-stabilized Al2O3 nanoparticles were achieved using 11-phosphonoundecanoic acid as a bifunctional linker, with the phosphonate group binding to the SiO2 substrate and the terminal carboxylate group binding to the nanoparticles. Subsequently, an Fe3O4 monolayer was formed over the Al2O3 layer using the same approach. The assembled Al2O3 and Fe3O4 nanoparticle monolayers acted as a catalyst support and catalyst, respectively, for the growth of vertically aligned CNTs. The CNTs were successfully synthesized using a conventional atmospheric pressure-chemical vapor deposition method with acetylene as the carbon precursor. Thus, these nanoparticle films provide a facile and inexpensive approach for producing homogenous CNTs

Privacy Preserving Large Language Models: ChatGPT Case Study Based Vision and Framework

The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical private information such as, context, specific details, identifying information etc. This have raised serious threats to user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy-preserving model for LLMs that consists of two main components i.e., preserving user privacy during the data curation/pre-processing together with preserving private context and the private training process for large-scale data. To demonstrate its applicability, we show how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy; specifically, we employed differential privacy and private training using Reinforcement Learning (RL). We measure the privacy loss and evaluate the measure of uncertainty or randomness once differential privacy is applied. It further recursively evaluates the level of privacy guarantees and the measure of uncertainty of public database and resources, during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, we hypothetically compared other mechanisms e..g, Blockchain, private information retrieval, randomisation, for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility etc. We conclude that differential privacy, randomisation, and obfuscation can impact utility and performance of trained models, conversely, the use of ToR, Blockchain, and PIR may introduce additional computational complexity and high training latency. We believe that the proposed model could be used as a benchmark for proposing privacy preserving LLMs for generative AI tools

SYNTHESIS AND IN SITU CHARACTERIZATION OF INTERCALATED TRANSITION METAL OXIDE NANOMATERIALS INVESTIGATED FOR NOVEL CATHODE APPLICATIONS

To develop an effective battery cathode material that can be useful for future batteries, the thermal stability and ion diffusion dynamics need to be well understood. In situ transmission electron microscopy (TEM) is a popular and proven technique to study the evolution of local structures during the dynamic processes in the cathode materials. This dissertation will demonstrate the application of high-resolution imaging and in situ heating and biasing in the TEM to study the structure and composition, morphology change, and ion diffusion in the cathode materials. The three chapters in this dissertation will be focused on the two cathode materials: zeta (ζ) vanadium pentoxide and chromium ion intercalated sodium manganese oxide. The first project demonstrates the effect of in situ heating method, nanowire size, sodium content, and vacuum condition on the thermal stability of zeta (ζ) vanadium pentoxide in real time in the TEM. The second project concentrates on in situ biasing in the TEM to study the sodium ion diffusion, silver exsolution, negative differential resistance phenomenon, and resistive switching characteristic in the zeta (ζ) vanadium pentoxide. The third project concentrates on the synthesis and characterization of chromium ion intercalated sodium manganese oxide. The works presented here show the capability of in situ TEM imaging techniques to study the dynamic changes in the structure and composition of the nanomaterials during the heating and biasing processes

Synergistic Ball Milling–Enzymatic Pretreatment of Brewer’s Spent Grains to Improve Volatile Fatty Acid Production through Thermophilic Anaerobic Fermentation

Brewer’s spent grain (BSG) as the major byproduct in the brewing industry is a promising feedstock to produce value-added products such as volatile fatty acids (VFAs). Synergistic ball mill–enzymatic hydrolysis (BM-EH) process is an environmentally friendly pretreatment method for lignocellulosic materials before bioprocessing. This study investigated the potential of raw and BM-EH pretreated BSG feedstocks to produce VFAs through a direct thermophilic anaerobic fermentation process without introducing a methanogen inhibitor. The highest VFA concentration of over 30 g/L was achieved under the high-solid loading fermentation (HS) of raw BSG. The synergistic BM-EH pretreatment helps to increase the cellulose conversion to 70%. Under conventional low TS fermentation conditions, compared to the controlled sample, prolonged pretreatment of the BSG substrate resulted in increased VFA yields from 0.25 to 0.33 g/gVS, and butyric acid became dominant instead of acetic acid

Premade Nanoparticle Films for the Synthesis of Vertically Aligned Carbon Nanotubes

Carbon nanotubes (CNTs) offer unique properties that have the potential to address multiple issues in industry and material sciences. Although many synthesis methods have been developed, it remains difficult to control CNT characteristics. Here, with the goal of achieving such control, we report a bottom-up process for CNT synthesis in which monolayers of premade aluminum oxide (Al2O3) and iron oxide (Fe3O4) nanoparticles were anchored on a flat silicon oxide (SiO2) substrate. The nanoparticle dispersion and monolayer assembly of the oleic-acid-stabilized Al2O3 nanoparticles were achieved using 11-phosphonoundecanoic acid as a bifunctional linker, with the phosphonate group binding to the SiO2 substrate and the terminal carboxylate group binding to the nanoparticles. Subsequently, an Fe3O4 monolayer was formed over the Al2O3 layer using the same approach. The assembled Al2O3 and Fe3O4 nanoparticle monolayers acted as a catalyst support and catalyst, respectively, for the growth of vertically aligned CNTs. The CNTs were successfully synthesized using a conventional atmospheric pressure-chemical vapor deposition method with acetylene as the carbon precursor. Thus, these nanoparticle films provide a facile and inexpensive approach for producing homogenous CNTs

Federated Learning-Inspired Technique for Attack Classification in IoT Networks

More than 10-billion physical items are being linked to the internet to conduct activities more independently and with less human involvement owing to the Internet of Things (IoT) technology. IoT networks are considered a source of identifiable data for vicious attackers to carry out criminal actions using automated processes. Machine learning (ML)-assisted methods for IoT security have gained much attention in recent years. However, the ML-training procedure incorporates large data which is transferable to the central server since data are created continually by IoT devices at the edge. In other words, conventional ML relies on a single server to store all of its data, which makes it a less desirable option for domains concerned about user privacy. The Federated Learning (FL)-based anomaly detection technique, which utilizes decentralized on-device data to identify IoT network intrusions, represents the proposed solution to the aforementioned problem. By exchanging updated weights with the centralized FL-server, the data are kept on local IoT devices while federating training cycles over GRUs (Gated Recurrent Units) models. The ensemble module of the technique assesses updates from several sources for improving the accuracy of the global ML technique. Experiments have shown that the proposed method surpasses the state-of-the-art techniques in protecting user data by registering enhanced performance measures of Statistical Analysis, Energy Efficiency, Memory Utilization, Attack Classification, and Client Accuracy Analysis for the identification of attacks

Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective

Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on the other hand, are subject to privacy and security risks due to design flaws. To achieve the desired performance, it is necessary to create a protected network. The goal of the current study is to look at recent privacy and security concerns influencing the network of drones (NoD). The current research emphasizes the importance of a security-empowered drone network to prevent interception and intrusion. A hybrid ML technique of logistic regression and random forest is used for the purpose of classification of data instances for maximal efficacy. By incorporating sophisticated artificial-intelligence-inspired techniques into the framework of a NoD, the proposed technique mitigates cybersecurity vulnerabilities while making the NoD protected and secure. For validation purposes, the suggested technique is tested against a challenging dataset, registering enhanced performance results in terms of temporal efficacy (34.56 s), statistical measures (precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73)

Analysis of purification of charged giant vesicles in a buffer using their size distribution

We have analyzed the purification of charged giant unilamellar vesicles (GUVs) prepared in a buffer containing various concentrations of salt using their size distribution. The membranes of GUVs were synthesized by a mixture of dioleoylphosphocholine (DOPC) and dioleoylphosphatidylglycerol (DOPG) lipids. The DOPG mole fractions (X) in the membranes of GUVs were 0.10, 0.25, 0.40, 0.55, 0.70, 0.90 in a physiological buffer containing 162 mM salt. In addition, for a fixed value of X the concentrations of salt (C) in the buffer were 12, 62, 112, 162, 212, 312, 362 mM. The size distribution histograms of experimentally investigated unpurified and purified GUVs were fitted with the lognormal distribution and obtained the multiplication factor $${\gamma }$$ for mean ($${\mu }$$) and $${\eta }$$ for standard deviation ($${\sigma }$$) of the lognormal distribution. The key parameters $$\gamma$$ and $$\eta$$ were responsible for changing the average size and size distribution of unpurified GUVs to purified ones. The theoretically fitting equation of experimentally obtained X- and C-dependent values of $$\gamma$$ and $$\eta$$ provided the calibration equation for estimating the average size of purified GUVs theoretically for any values of X and C. The estimated size of purified GUVs increased with the increase in electrostatic effect (i.e., increase in vesicle surface charge density or decrease in salt concentration in buffer). The estimated size of purified GUVs varied with X and C, which supported the previous report qualitatively. These investigations might be helpful in the field of cell/chemical biology for understanding the process of purification of vesicles/cells investigated by any other techniques