Characterization of cadmium-doped nZVI residuals: Structure, morphology, and photoelectrochemical properties

Serum concentrations of selenium, selenium species and metals in children newly diagnosed with leukemia: a hospital-based case-control study.

SiC/Si heterostructure exhibits significant potential for applications in advanced electronics and optoelectronic devices, owing to its exceptional thermal and electrical properties. A direct bonding method utilizing plasma to activate surface in a vacuum environment has been developed for SiC/Si heterogeneous integration. The necessity of a vacuum environment reduces the efficiency of plasma-activated bonding (PAB), thus limiting its application in heterogeneous integration. In this study, we investigated the high-efficiency and low-temperature direct bonding of SiC/Si via atmospheric inductively coupled plasma (ICP). The mechanism of ICP activation and direct bonding of SiC/Si pairs was investigated. After less than 5 s of high-energy Ar ICP irradiation, the intrinsic bonds of surface were disrupted, leading to surface activation. The activated surface readily adsorbed hydroxyl groups from a humid environment, resulting in a superhydrophilic (contact angle <3°) surface. This facilitated a dehydration condensation reaction at low temperature (≤250 °C), enabling direct bonding. The original surface conditions were maintained during the activation process and met the requirements for direct bonding. Additionally, ICP irradiation effectively eliminated surface contaminants and enhanced the quality of direct bonding. Using optimized process parameters, the bonding efficiency is more than 99% and the maximum bonding strength is more than 6 MPa. The results of transmission electron microscopy (TEM) demonstrated that the bonding interface was dense and low-defect. Thus, ICP irradiation can efficiently activate the surface and improve the bonding quality without a vacuum environment. The direct bonding of SiC/Si heterostructures through ICP irradiation holds significant potential for the fabrication of high-performance power electronics and micro/nanofluidic devices.

Halogen detection, especially in a multielement fashion and with liquid chromatography, is challenging because of the fundamental limitations of ionization in inductively coupled plasma (ICP) mass spectrometry. Post-plasma ionization alleviates these limitations. For Cl detection, plasma-assisted HCl formation followed by chemical ionization using barium-containing reagent ions has been reported, yielding BaCl+ and offering simultaneous detection with other halogens (e.g. F detection using BaF+). However, the broad reactivity of barium-based ionization also makes it susceptible to interference from other plasma-generated species, increasing the potential for ionization suppression, sensitivity loss, and elevated matrix effects. Here, we report the development of HCl-selective post-plasma ionization reactions to improve ionization robustness and matrix tolerance in Cl detection. We evaluate a series of metal ions (M) with aqueous-phase affinity for chloride to determine their potential to form MCl(NO3)n+ (n = 0–1) in the post-ICP region. We show that metal ions follow the order Pb2+, Bi3+, Cd2+ > In3+ > Zn2+, Fe3+ >> Ba2+ in terms of propensity of their reagent ions to react with HCl in the presence of interfering plasma products such as HNO3 and HNO2. Among the metal ions, Pb2+ also offers the highest sensitivity for Cl detection because of efficient reagent ion generation by electrospray ionization. The robustness of PbCl+ detection is also tested in P-containing matrices, revealing a 5-fold improvement relative to that with BaCl+ and further confirming improved matrix tolerance due to more selective ionization reactions. These studies offer fundamental insights and pave the way towards robust multielement methods for halogen detection in speciation analyses.

Investigation of aluminum nitride-based ferroelectric thin films for non-volatile memory applications has largely focused on various thin film solid solutions grown by reactive sputtering. The growth process leads to significant DC electrical leakage related to mosaic disorder and point defects in this class of materials; extrinsic alloying elements such as scandium or boron are used to facilitate ferroelectric switching at lower electric fields to limit these deleterious effects. We take a different approach focusing on growth via atomic layer annealing using a nitrogen remote inductively coupled plasma (ICP). We demonstrate ferroelectric behavior in nanocrystalline wurtzite aluminum nitride (AlN) films with neither additional alloying components nor post-process annealing grown using a 350 °C CMOS-compatible growth process. The films do not exhibit hard dielectric breakdown even under electric fields in excess of 10 MV cm-1. Electrical property characterization using positive-up-negative-down (PUND) measurements shows remanent polarization (Pr) in excess of 30 µC cm-2. Piezoresponse force microscopy (PFM) DC bias poling experiments yield behavior consistent with ferroelectricity. Structural characterization was performed using scanning/transmission electron microscopy, X-ray photoelectron spectroscopy depth profiling, and spectroscopic ellipsometry. An ALD-based growth approach to ferroelectric aluminum nitride-based films holds significant advantage from a device scaling standpoint and provides an alternative route towards aluminum nitride-based thin films.

Abstract Silicon oxynitride (SiON) thin films are widely used as mask materials for amorphous carbon layer (ACL) patterning, where high etch selectivity and stable profile control are required. In this study, the etching characteristics of SiON mask layers were systematically investigated using C₃F₆/C₄F₈/He plasmas in a 2 MHz bias inductively coupled plasma (ICP) system, with direct comparison to conventional CF₄-based chemistry under ultra-low-pressure conditions. Despite its significantly lower global warming potential (GWP), the C₃F₆-based plasma exhibited SiON etch rates comparable to those of CF₄ while consistently providing enhanced selectivity to photoresist over a wide range of gas-mixing conditions. Representative cross-sectional SEM images confirmed that anisotropic and well-preserved SiON profiles were maintained, indicating that the improved selectivity was achieved without compromising profile integrity. Plasma diagnostics and surface analyses suggest that the enhanced selectivity originates from controlled fluorocarbon surface reactions without excessive polymer accumulation. In addition, exhaust-line FTIR analysis combined with mass-manufactured total CO₂ equivalent (MMTCE) evaluation revealed reduced greenhouse gas emission characteristics for the C₃F₆-based process compared to CF₄. These results indicate that C₃F₆ is a viable low-GWP alternative to CF₄ for SiON mask etching, offering improved selectivity, stable profile control, and reduced environmental impact in ACL-related fabrication processes.

Occurrence profiles of 43 minerals in different types of Tibetan tea determined by ICP-MS and the associated risk assessment

Genetic disorders involving metal metabolism dysregulation pose significant challenges in diagnosis, treatment, and monitoring. Traditional diagnostic methods, such as genetic sequencing and biochemical assays, often fail to detect early metabolic imbalances, delaying interventions. Inductively Coupled Plasma (ICP) Spectroscopy, including ICPMass Spectrometry (ICP-MS) and ICP-Optical Emission Spectroscopy (ICP-OES), has emerged as a highly sensitive and precise tool for analyzing trace metal concentrations in biological samples, offering new insights into metalassociated genetic disorders. This article explores the role of ICP spectroscopy in early diagnosis, biomarker discovery, and treatment monitoring for disorders such as Wilson's disease (copper accumulation), hemochromatosis (iron overload), Menkes disease (copper deficiency), and lead poisoning-related neurodevelopmental disorders. ICP-MS enables the detection of ultratrace metal levels, ensuring early intervention before clinical symptoms appear. Additionally, ICP techniques facilitate personalized medicine approaches, allowing for individualized treatment plans based on a patient's metal homeostasis profile. Recent advancements in HR-ICP-MS, single-cell ICP-MS, and laser ablation ICP-MS have further expanded the applications of ICP spectroscopy in genomic and proteomic research, enabling detailed elemental mapping and improved disease modelling. The integration of ICP spectroscopy with omics technologies is paving the way for precision medicine, optimizing treatments for genetic disorders at an individualized level. As ICP technology continues to evolve, it holds immense potential for advancing genetic disorder research, improving diagnostic accuracy, and enhancing therapeutic strategies, ultimately transforming the landscape of metallomics-based medicine.

Public automobile parks' soil dust is a significant source of inhalable particulate matter in metropolitan environments worldwide. This study aims to examine the health risks associated with ten potentially toxic elements (PTEs) (As, Ba, Cd, Cr, Cu, Ni, Hg, Li, Zn and Pb) and their composition in 13 different motor parks in the Northwest region of Nigeria. The samples were digested with acids and analysed using an Inductively Coupled Plasma Mass Spectrometer, while cold vapour atomic fluorescence spectrophotometers were used to analyse mercury. The highest mean concentrations followed the sequence Ba (189mg/kg), followed by Zn (157mg/kg), Cu (115mg/kg), Cr (58.93mg/kg), Ni (34.27mg/kg), Pb (23.72mg/kg), Cd (9.63mg/kg), Li (1.07mg/kg), and Hg (0.08mg/kg). Ba and Zn exhibited the highest enrichment factor (EF) and contamination factor (CF). The health risk assessment for PTEs showed that As, Pb, Cr, and Ba have the greatest health index, suggesting a possible health risk where ingestion is the primary pathway, with children having higher vulnerability than adults. The geo-accumulation index reflected different pollution levels, with certain elements presenting serious ecological risks. The study also revealed different pollution patterns in automobile parks by comparing its findings with those of other studies conducted around the world. Principal Component Analysis (PCA) identified the sources of PTEs in the motor parks' dust includes human activities, vehicular emissions and lithogenic occurrences through leaching and runoffs. The study further showed that metals particularly Cr present slight to high ecological risks. Health hazard evaluation uncovered that the occupants of the area particularly children are more inclined to non-cancer-causing health risks. The study highlights the necessity of implementing remedial measures to address the environmental and public health problems associated with metal pollution.

Porous thin-film getters are extensively utilized in the field of MEMS vacuum packaging. Nevertheless, their effectiveness is frequently constrained by the comparatively modest effective surface area of conventional planar structures. In this work, a porous Au/Ti thin-film getter based on a three-dimensional black silicon scaffold is developed to enhance the effective surface area and improve gettering performance. The fabrication of black silicon nanostructures is achieved through an SF6/O2-based inductively coupled plasma (ICP) etching process, followed by the deposition of Au/Ti bilayer films by DC magnetron sputtering. The morphological evolution of the Ti film on the nanostructured substrate and the activation behavior of the Au/Ti bilayer are systematically investigated using scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS). The results demonstrate that the shadowing effect during sputtering leads to the formation of a porous film with increased surface roughness and an open structure. XPS analysis demonstrates that there is a significant increase in the oxygen content on the surface at higher activation temperatures. This suggests that effective sorption capability is achieved following activation. In comparison with planar substrates, the three-dimensional black silicon scaffold has been demonstrated to promote the formation of a more open and functional structure. The results obtained from this study indicate that the proposed fabrication strategy offers a feasible and MEMS-compatible approach for the construction of porous thin-film getters, thereby enhancing their effective surface area.

Lithium-ion encapsulated fullerene (Li+@C60) represents a novel class of ionic endohedral metallofullerenes possessing distinct electronic properties, including high ionic conductivity and superior electron-accepting capabilities compared to pristine C60. While the "plasma shower" ion implantation method has enabled the continuous synthesis of Li+@C60, the industrial application of this material is currently impeded by a critical disparity between the theoretical synthesis yield and the actual recovered yield. Current extraction protocols typically recover only approximately 0.8% of Li+@C60, which is significantly lower than theoretical predictions. This study aims to rigorously quantify the Li+@C60 content in the postsynthesis crude soot to determine the true efficiency of the plasma shower synthesis and identify the physicochemical factors limiting extraction. We employed a multifaceted analytical approach combining Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS), solid-state 7Li Nuclear Magnetic Resonance (NMR) spectroscopy, and Inductively Coupled Plasma (ICP) analysis. Quantitative analysis based on three independent synthesis runs (N = 3) reveals that the crude soot contains Li+@C60 at a mass percentage of 3.4 ± 0.1%. Furthermore, spectral analysis identified a significant abundance of oxidized derivatives (Li+@C60O, 4.3 ± 0.1%), indicating a total encapsulation efficiency of 7.7% ± 0.1%. Additionally, Transmission Electron Microscopy (TEM) revealed the formation of robust clusters with a median diameter of approximately 8 nm. Collectively, these findings confirm that the low recovery in conventional methods (∼0.8%) is not due to synthesis failure, but rather due to the formation of insoluble aggregates and oxidative derivatives. This report provides a detailed quantitative framework for evaluating Li+@C60 synthesis and proposes that optimizing physical disintegration techniques, such as dual-frequency ultrasonication, alongside strict oxidation control, is essential for bridging the yield gap.

Abstract Low-temperature plasmas (LTPs) have extensive applications in numerous fields. However, revealing the relationships between operational and characterization parameters remains challenging. Dimensional analysis enables the extraction of dimensionless numbers from complex physical systems, thereby identifying the crucial factors governing system behaviours. Recently proposed data-driven dimensional analysis methods integrate neural networks and are capable of discovering dominant dimensionless numbers. Neural network performance critically depends on hyperparameters configuration, yet tuning them demands expert knowledge and seldom yields globally optimal results. To address this challenge, this study introduces an automated machine learning (AutoML) mechanism that employs a Treestructured Parzen Estimator Sampler (TPESampler) to automatically search for optimal neural architectures within a custom hyperparameter space. The proposed method is applied to two representative LTPs cases: inductively coupled plasma (ICP) and plasma-catalytic dry reforming of methane (DRM). The results demonstrate that the optimized neural networks achieve excellent performance according to multiple evaluation metrics in both cases. The discovered dominant dimensionless numbers significantly influence both the ionization degree and energy conversion efficiency in LTPs, and the constructed polynomial expressions of characterization parameters demonstrate high prediction accuracy. This method provides a widely applicable and interpretable strategy to identify key operational parameters in complex plasma systems, greatly aiding the optimization and design of LTPs processes.

As artificial intelligence (AI) and high-performance computing demand greater parallel data processing, next-generation memory architectures must also evolve. High Bandwidth Memory (HBM) supports this with its 3D-stacked structure and wide I/O interface, but further improvements in power efficiency and thermal management are still required. In addition, microbumps currently used for chip stacking face scalability limitations. To address these challenges, hybrid copper (Cu) bonding has been proposed as a promising solution. This study presents a surface treatment strategy for low-temperature Cu-to-Cu direct bonding using CH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub>-based reductive plasma: (1) native Cu oxides were removed, and (2) re-oxidation was suppressed by forming a hydride carbon passivation layer. A design of experiments (DOE) approach was used to optimize ICP (inductively coupled plasma) power, CH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> flow rate, and pressure. Surface analysis was performed using XPS, TOF-SIMS, TEM, and sheet resistance measurements, while post-bonding evaluation included TEM, SEM, and shear strength analysis. The results demonstrate that the energy of radicals and ions plays a decisive role in Cu oxide reduction, contributing to effective surface activation. The resulting passivation layer was confirmed to be a hydrogenated amorphous carbon (a-C:H) film rich in C, CH, and C2H species. Bonding was conducted at 260 °C and 15 MPa for 1 hour, followed by post-annealing at 200 °C. TEM analysis revealed a void-free and oxygen-free bonding interface with the presence of carbon, suggesting that the carbon layer acted as a passivating layer. These findings confirm that reductuve CH<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> plasma treatment is highly effective for enabling low-temperature Cu bonding, and highlight the strong potential of this approach for next-generation hybrid bonding technologies.

Replicating the endogenous electromechanical microenvironment of bone remains a significant challenge in regenerative medicine. This study aims to develop a promising scaffold by integrating piezoelectric K0.5Na0.5NbO3/poly (KNN) nanoparticles into poly (L-lactic acid) (PLLA) nanofibers to promote bone healing. KNN/PLLA nanofibers were electrospun and verified via X-ray diffraction (XRD). Scanning Electron Microscope (SEM) was used to characterize morphology and assess biocompatibility on 9 wt% KNN/PLLA. The distribution of KNN was analyzed via energy dispersive spectroscopy (EDS). The mechanical properties were evaluated through Universal Testing Machine (UTM). Piezoelectric properties were quantified using an electrostatic voltmeter and Piezoresponse Force Microscopy (PFM), while Niobium (Nb) ion release was measured via inductively coupled plasma (ICP) analysis. Osteogenic differentiation was evaluated through cell proliferation, quantitative real-time PCR (qRT-PCR) for osteogenic markers osteocalcin (OCN) and runt-related transcription factor 2 (RUNX2), alkaline phosphatase (ALP) and Alizarin Red S (ARS) assays for osteogenesis, and tube formation for angiogenesis. XRD confirmed successful KNN loading. Tensile tests showed that KNN incorporation enhanced mechanical properties. ICP analysis detected Nb release, reflecting the degradation. Increasing KNN content reduced fiber diameter and enhanced piezoelectricity. SEM verified biocompatibility via cell growth on 9 wt% KNN. Notably, KNN loading dose dependently upregulated OCN and RUNX2 expression, enhanced ALP activity and ARS staining, and promoted angiogenesis. The 9 wt% KNN/PLLA nanofibers exhibited superior physicochemical and mechanical properties, a sevenfold increase in piezoelectric output. The nanofibers significantly enhanced bone regeneration, evidenced by upregulated osteogenic markers (OCN/RUNX2) and markedly improved ALP activity (60%) and ARS mineralization (70%). Coupled with favorable degradation and enhanced angiogenesis, the nanofibers demonstrate high potential for functional bone tissue engineering.

The mid-Norwegian margin is one of the best studied volcanic rifted margins on Earth. Geophysical investigations have demonstrated the presence of well-developed inner and outer Seaward Dipping Reflectors (SDRs), landward flows, lava deltas, marginal highs, volcanic centers, ash layers, and sill complexes. These features have been proven to consist of magmatic rocks through the international Deep Sea Drilling Program (DSDP Leg 38, 1974), Ocean Drilling Program (ODP Leg 104, 1985), International Ocean Discovery Program (IODP Expedition 396, 2021), and commercial drilling. A total of fifteen drill cores penetrated magmatic rocks that formed between 57 and 50 million years ago (Ma). Here we provide (i) new (n = 224) major and trace element compositions obtained by X-ray fluorescence (XRF), inductively-coupled plasma mass spectrometry (ICP-MS), and inductively-coupled optical emission spectrometry (ICP-OES) on whole rock powders of magmatic rocks for IODP Exp. 396 (n = 119), ODP Exp. 104 (n = 79), DSDP Exp. 38 (n = 24); and (ii) a compilation of all new and published data for magmatic rocks in the fifteen drill cores (n = 563). Portable X-ray fluorescence (pXRF) data (n = 381) for the IODP Exp. 396 cores are also reported. These datasets provide a resource for examining the origin of magmatism associated with continental breakup and rifted margin formation, particularly the formation of excess magmatism compared to normal mid-oceanic spreading ridges, mantle-crust interaction, and the linkage of magmatism to global hyperthermal events on Earth's surface.

A state-of-the-art, robust classification model using artificial intelligence (AI) algorithms was developed in this study to estimate the gold ore classes based on trace elements measured by inductively coupled plasma (ICP). This approach involves the utilisation of stand-alone machine learning (ML) algorithms integrated within a committee machine (CM) framework. To this end, 19 trace elements including arsenic, bismuth, cadmium, cobalt, chromium, copper, iron, mercury, manganese, molybdenum, nickel, lead, antimony, selenium, tin, strontium, thorium, uranium and zinc acquired from eight drill holes in the Sari-Gunay gold-polymetallic mine (SGGM), Iran, were used as the inputs and three gold grade classes (ore, low-grade ore, and waste) were specified as outputs classes. The classes were defined based on their gold grades and mineable zones, using threshold values of > 1g/t, 0.5-1g/t, and < 0.5g/t for ore, low-grade ore and waste, respectively. Eight stand-alone ML-based classifiers, including the back-propagation neural network (BP-NN), Takagi and Sugeno fuzzy inference system (TS-FIS), adaptive boosting (AdaBoost), GentleBoost, LogitBoost, random under-sampling boosting (RUSBoost), extreme gradient boosting (XGBoost) and adaptive neuro-fuzzy inference system (ANFIS) were used to perform the initial classification. The accuracy, precision and recall metrics were employed to evaluate the performance of the algorithms by comparing predicted results with reference data derived from ICP analysis and geological studies, providing a consistent reference benchmark. The parameters within each algorithm were tuned using the parameter-tuning method, aiming to identify the optimal model. The AdaBoost algorithm outperformed the other stand-alone algorithms. To further improve the stand-alone-derived classification outcomes and integrate the algorithms into a unified algorithm, genetic (GA) and simulated annealing (SA) optimisation algorithms were configured in the framework of the CM. Parameter-tuning of both optimisation algorithms produced multiple distinct models to determine the optimal weights and the best-performing model. The CM with SA (CMSA) outperformed the GA optimiser. Employing CMSA resulted in a 7.28% improvement in overall accuracy compared with the average performance of the stand-alone algorithms.

Thermal modification of wood at low-level temperatures is increasingly adopted as a sustainable alternative to chemical preservation for improving dimensional stability and durability. However, thermochemical processing of biomass can emit fine particulate matter (PM₂.₅) laden with potentially toxic elements (PTEs), creating significant risks to both environmental health and occupational safety. This study quantified PM₂.₅-bound PTE emissions during lowtemperature thermal wood processing and evaluated their atmospheric transport and health implications. A multi-phase methodology integrated gravimetric PM₂.₅ sampling using PTFE filters, hotplate wet acid digestion, and determining the concentration of the PTEs using an Inductively Coupled Plasma Optical Emission Spectrometer. Ambient occupational PM₂.₅ concentrations were calculated from filter mass differentials and sampled air volumes. A mechanistic Thermo– Particulate Metal Fate and Transport Model (TPM-FTM) was developed to couple thermochemical emission processes, particle–metal partitioning, atmospheric dispersion, deposition, and receptor exposure. Model performance was evaluated using statistical metrics, and uncertainty propagation was assessed through Monte Carlo simulation. Detectable concentrations of PTE-associated PM₂.₅ were observed under low-temperature operational conditions, with size-resolved partitioning influencing atmospheric mobility and inhalation exposure. Occupational environments exhibited higher exposure levels compared with near-field community locations. Evaluations against established regulatory standards confirmed that exposure to these emissions poses no significant carcinogenic or non-carcinogenic health risks, with values generally falling within acceptable limits; localized emission intensities and ventilation conditions increased exposure variability. This study provides an integrated experimental–modeling framework for assessing particulate-bound metal emissions from thermally modified wood processing and offers evidence-based guidance for emission mitigation, occupational safety management, and regulatory evaluation.

Inductively coupled plasma (ICP) etching of N-polar n-doped gallium nitride (GaN) with a CH4/H2/Ar chemistry was investigated as a function of CH4 ratio in the gas mixture, DC bias voltage, ICP source power, pressure, and total gas flow. Two etching modes are evidenced when varying CH4 ratio in the gas mixture, the DC bias voltage at fixed source power or the total gas flow. Moreover, the etch rate increases with increasing DC bias voltage at 1000 W source power, increasing source power or decreasing pressure. In some conditions, the formation of a polymer film onto the GaN surface was observed. Etch rates as high as 65 nm/min was achieved. Based on the competition between GaN etching and polymer layer deposition observed from the etch rate study, an etching mechanism of GaN with a CH4/H2/Ar chemistry is proposed. This study proposes an overview of etching performances of GaN with both Cl2/Ar and CH4/H2/Ar chemistries. The luminescence properties and GaN surface chemistry for a given set of plasma parameters in the CH4-based chemistry were investigated by cathodoluminescence, x-ray photoelectron spectroscopy, and hard x-ray photoelectron spectroscopy, respectively. The GaN properties after etching were compared to the ones obtained after a Cl2/Ar etching chemistry. Preliminary results suggest that the CH4-based etched samples exhibit improved electro-optical performance.

The neonatal period is highly susceptible to metabolic impairments that may persist throughout lifespan and predispose to increased obesity risk and related complications. During these first stages of life, trace elements and heavy metals play a central role in regulating health status and participating in obesity pathophysiology. Thus, we hypothesize that neonatal metrics might serve as reliable predictors of obesity-related metal alterations occurring later in childhood. This study relies on a population comprising children with obesity (N = 79, age range: 6–14 years), from whom birth metrics (i.e., gestational age, length, and weight at birth) were registered from medical records and blood samples were collected to evaluate classical metabolic markers (insulin and glucose metabolism, inflammatory status) and metal biodistribution by inductively-coupled plasma mass spectrometry. Interestingly, higher gestational age and length at birth were associated with lower inflammation, insulinemia, and glycemia in childhood, as well as with lower levels of toxic heavy metals (i.e., arsenic, cadmium, lead) and greater levels of essential trace elements (i.e., zinc, selenium). Conversely, higher body mass index at birth predicted exacerbated failures in glucose homeostasis and an unfavorable multi-elemental profile, as reflected in negative associations with minerals involved in endocrine control (i.e., zinc, chromium, molybdenum, selenium). In summary, this study supports that even slight deviations in neonatal parameters might influence metabolic health later in life, considering both classical clinical markers (e.g., insulin resistance, inflammation) and complementary metallomics assessments.

Inductively coupled plasma (ICP) etching is a key step in device fabrication of AlGaN-based deep ultraviolet (DUV) micro light-emitting diodes (Micro-LEDs). By optimizing the radio frequency (RF) power parameters during the ICP etching process, an appropriate RF power could be adopted to reduce etching damage and light loss during the light transmission process. It can be obtained in this work that employing RF power that is too large during the ICP process will introduce more defects into the active region, which will form more nonradiative recombination centers and reduce carrier lifetime and injection efficiency. However, employing RF power that is too small will lead to smaller etching angles, which will increase light loss during the light transmission process. In this study, AlGaN-based DUV Micro-LED arrays with an emission wavelength of 278 nm were fabricated using an optimal RF power of 100 W. This approach significantly mitigated both sidewall damage and optical transmission loss, resulting in a 17.4% enhancement of light output power.