Operational insights and analyses of an integrated subsea CO2 injection system

Substitute materials that accurately reproduce the radiological properties of human tissues are required for direct in vivo measurement of internally deposited radioactive materials to estimate associated health risk, especially for the respiratory tract. The Livermore torso phantom, the de facto standard for calibrating detector systems that measure radioactive materials deposited in the lungs, liver, and thoracic lymph nodes, was designed with tissue substitute materials that match the density and attenuation coefficient exhibited by natural human tissue when exposed to single low-energy x rays associated with the decay of plutonium. In this study, we evaluated the radiometric tissue equivalence of new tissue substitutes for muscle, rib, sternum, lung, and cartilage that are suitable for a continuous low photon energy spectrum from approximately 30 to 120 keV. The formulation for each of the tissue substitutes was developed using a novel method that determines the optimized quantity of base material and additives to produce a material that best matches the density and photon transmission exhibited by the natural human tissue present in the thoracic cavity. Measurements of the mass attenuation coefficient (i.e., ) from approximately 30 keV up to 120 keV for each substitute tissue were within 8% or better to expected values calculated using the photon cross section database XCOM from the National Institute for Standards and Technology.

Aberrant DNA methylation biomarkers have demonstrated potential for early cancer detection, multicancer detection, and determining the tissue of origin. Due to their stability, frequency, and accessibility in bodily fluids, circulating cell-free DNA (cfDNA) methylation is a promising biomarker in liquid biopsy. A reliable and quantifiable analysis of cfDNA methylation status is critical to its application. However, there are current challenges and a lack of consensus on measurement methods. To address this, we developed two candidate methylated cfDNA reference materials (RMs). The National Institute of Standards and Technology (NIST) RM consists of five components, formulated by mixing in vitro methylated cfDNA simulant at fractions of 0%, 5%, 25%, 50%, and 100% with native-state cfDNA simulant derived from the GM24385 cell line. The LGC Clinical Diagnostics (LGC) RM consists of two components: non-methylated cfDNA simulant derived from GM24385 genomic DNA and whole genome amplification and methylated cfDNA produced by in vitro methylation of amplified material. The candidate RMs were characterized, and the methylation status of three targets was confirmed by droplet digital PCR (ddPCR) assays. To test the utility of these RMs, six laboratories participated in an interlaboratory study, each using their own lab-developed assays and methods, which included methylation-specific qPCR, nanoplate digital PCR (dPCR), ddPCR, matrix methylated DNA immunoprecipitation-based assays, and whole-genome bisulfite sequencing. The interlaboratory study results showed that the designed percentage of methylation was well correlated with the observed values across all participating labs, and good reproducibility was found for each individual method. However, slightly different methylation proportions associated with assay-specific biases were observed. This study clearly demonstrates the value of candidate RMs as standards for evaluating assay performance, as well as for increasing confidence in reporting cfDNA methylation status for clinical applications.

Long-term peritoneal dialysis (PD) triggers colonic dysfunction and multiple complications, yet the underlying pathogenesis and effective therapies remain elusive. We established a long-term peritoneal dialysis fluid (PDF)-treated mice model to explore molecular alterations via untargeted gas chromatography‒mass spectrometry (GC‒MS) and the data were analysed by MetaboAnalyst 5.0. After validating features against the National Institute of Standards and Technology (NIST) library and Human Metabolome Database (HMDB) libraries, we used MetaboAnalyst 5.0 to pinpoint the key discriminating metabolites and their enriched pathways, and we found abnormal arginine metabolism was closely related to colonic dysfunction. Subsequently, liquid chromatography-tandem mass spectrometry (LC‒MS/MS) and western blotting were used to determine abnormal arginine metabolism in colonic dysfunction. Additionally, the benefits of arginine were determined in PDF-treated T84 cells and the PDF-treated mouse model, evaluated the effects by histopathological staining and western blotting. Untargeted metabolomics revealed that long-term PD disrupts multiple colonic pathways, especially the arginine pathway. LC-MS/MS confirmed a marked fall in colon arginine after long-term PDF exposure. Western blot showed the downregulation of argininosuccinatelyase (ASL) and the upregulation of arginase-1 (Arg-1). Further study found that supplementing exogenous arginine to T84 cell and to PDF-treated mice reinstated mucosal morphology, restored tight junction (TJ) proteins expression, and reversed PDF-induced epithelial-barrier injury. Long-term PD leads to the injury of colonic barrier and metabolic disturbance, especially the metabolism of arginine, and exogenous arginine supplementation rescues the colon injured by PD.

Drug mixture composition and compound identifications provide public health, first responder, and law enforcement communities with critical and actionable data, guiding emergency response and interdiction, informing the public, and targeting overdose prevention. The advent of novel synthetic opioids, nitazenes, and benzodiazepines, along with the spread of strong veterinary tranquilizer adulterants (e.g., xylazine and medetomidine), have created an ever-changing drug landscape. The Testing, Rapid Analysis, and Narcotic Quality (TRANQ) Research Act of 2023 directs research, method development, measurement science, and standards to address these hurdles. In conjunction with the National Institute of Standards and Technology (NIST) Rapid Drug Analysis and Research (RaDAR) program, aimed at monitoring the chemical makeup of the drug landscape, we are investigating analytical instrumentation to enable drug screening to move from the laboratory to an agile point-of-need setting. We explored compound identification with a direct analysis in real time triple quadrupole mass spectrometer (DART-TQ-MS) and the NIST/NIJ DART-MS Data Interpretation Tool with Forensics Database. Full scan analysis of single-component standards demonstrated limits of detection generally in the tens to hundreds of picograms with a few in the single nanogram range (i.e., compounds with extremes in volatility). Single-component, mixtures, and real-world street drug samples were investigated with potential identifications made matching against the high-resolution mass spectral library, using both full scan and product ion scan spectra. The development of a compact TQ-MS system enables the future potential for rapid on-site analysis, supporting public health, law enforcement, and forensic applications. This innovation paves the way for mobile, point-of-need drug testing and identification, enhancing our ability to respond to emerging drug threats.

This study proposes a fully hardware-based reservoir computing (RC) system, which utilizes a single ferroelectric thin-film transistor (FeTFT) based on a Si3N4/Hf0.5Zr0.5O2 (HZO)/indium gallium zinc oxide (IGZO) tri-layer stack. The proposed FeTFT operates in three independent memory modes according to two different input signals, electrical stimulation and optical stimulation: electric long-term (LT), electric short-term (ST), and optical ST. Through this, non-volatile memory characteristics and volatile memory characteristics were successfully integrated within a single device. The non-volatile electric LT mode is based on the ferroelectric polarization switching mechanism of the HZO layer and was utilized as the readout layer for the RC, demonstrating excellent endurance and retention characteristics. The volatile electric ST mode is based on the charge trapping mechanism of the Si3N4 trap layer, while the optical ST mode is based on the optical ionization mechanism of the IGZO channel. These two ST modes perform the function of a 4-bit reservoir layer with short-term characteristics such as paired-pulse facilitation. Finally, the fully hardware-based RC system, which organically integrates these three modes and applies the hybrid mapping model, achieved a high recognition accuracy of 92.43% in the Modified National Institute of Standards and Technology dataset recognition task.

Comparative development of volatile-oriented multi-SPME and derivatisation-based GC×GC-TOFMS workflows for non-targeted faecal metabolomics.

The memtransistor constructed using the emerging two-dimensional tellurene material has demonstrated significant potential for application in artificial synaptic devices and image recognition. However, conventional device fabrication processes, such as dry transfer, restrict tellurene's further application in flexibility and wearable electronics. Here, a fully printed, flexible tellurene field-effect transistor (FET) is demonstrated, consisting of a channel, gate dielectric layer, and contact electrodes, all of which are prepared using functional inks that include tellurene, h-BN, and graphene, respectively. Such a device integrates scalable ink formulations and neuromorphic functionality for advanced electronics, exhibiting relatively stable electrical performance even after 10,000 bending cycles at a curvature radius of 11.05 mm. Applying electric stimulation to the h-BN layer enables the realization of a bioinspired memtransistor, achieving paired-pulse facilitation, reconfigurable short-term plasticity to long-term plasticity transitions, and synaptic weight updates. Moreover, image recognition simulation using an artificial neural network achieves 93.91% accuracy on the Modified National Institute of Standards and Technology database, which can maintain 78.33% accuracy after introducing σ = 0.7 Gaussian noise. These results position printed tellurene FETs as promising, noise-resilient building blocks for scalable, flexible neuromorphic systems.

Abstract Light‐stimulated synaptic transistors offer a promising platform for neuromorphic computing and artificial vision by emulating biological synaptic behaviors with optical control. In this study, we demonstrate a photonic synaptic transistor based on an organic semiconductor system, exhibiting tunable excitatory postsynaptic current, paired‐pulse facilitation, and synaptic weight modulation under varying light intensities and pulse durations. The underlying mechanism is attributed to oxygen‐induced charge trapping, as confirmed by electronic structure analysis and Kelvin probe force microscopy. Furthermore, the device is integrated into an artificial neural network for delay reservoir computing, achieving high recognition accuracy in Modified National Institute of Standards and Technology digit classification. These findings highlight the potential of light‐driven neuromorphic hardware for energy‐efficient, high‐speed, and flexible artificial intelligence applications, paving the way for the development of next‐generation optical neuromorphic processors.

Comparison of anticipated and detected drug contents in samples submitted to a statewide drug checking program in Maryland, USA.

Abstract A hardware-bound key management architecture is developed, which is used in digital currency system with controlled access, verifiable node identity and high reliability encryption protection. Firstly, random input generation, polynomial coefficient construction, share encapsulation and commitment verification are all limited within the isolated execution boundary of hardware security module, thus separating the key generation path from the untrusted host environment. Secondly, the distributed key generation (DKG) mechanism is combined with the threshold signature workflow of DKG aggregation based on flexible turn optimization Schnorr threshold, allowing multiple nodes to complete collaborative signature without exposing local key shares. Through layered communication, controlled state evolution and certified standby node taking over, life cycle synchronization, multi-area recovery and disaster recovery switching are realized, thus ensuring the continuity of the system under node failure and regional failure. The results show that the average entropy reaches 0.99795 bit/bit under the condition of 16 nodes and 40 independent experiments for each node, while the test pass rate according to National Institute of Standards and Technology (NIST) Special Publication (SP) 800-22 remains at 96.4%. In the case of 30% malicious coefficient injection, the hardware-bound DKG structure still maintains the verification pass rate of 89.1%. When the proportion of abnormal nodes rose to 40%, the completion rate of DKG remained at 94.1%. The blocking rates of share substitution attack and offset injection attack reach 98.7% and 97.4% respectively. In the performance evaluation, the elliptic curve cryptography operation is accelerated by 3.8 times to 4.5 times with the assistance of hardware security module, and the number of transactions per second can reach 1165 under high load conditions. The proposed architecture enhances the execution integrity, key isolation and operation continuity of multi-node digital currency infrastructure. This design provides a reproducible and fault-tolerant security path for trusted digital currency deployment.

An interlaboratory comparison study was conducted among five laboratories for determining 24,25-dihydroxyvitamin D3 (24,25(OH)2D3) in human serum using liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods. The Centers for Disease Control and Prevention (CDC), Imperial College Healthcare NHS Trust, University College Cork, University of Liège, and University of Washington analyzed 50 single-donor samples and two new Standard Reference Materials (SRMs®). The results from each laboratory were compared with target values assigned by the National Institute of Standards and Technology (NIST) using a reference measurement procedure (RMP) and evaluated using Ordinary Deming linear regression and Bland-Altman analysis. Three of the five laboratory methods provided results that were in good agreement with the NIST RMP results showing linear regression slopes ranging from 0.972 to 1.003 and Bland-Altman mean bias of -0.092nmol/L, 0.025nmol/L, and 0.035nmol/L. Two laboratories demonstrated a significant positive bias with linear regression slopes of 1.158 and 1.214 and Bland-Altman mean bias of 0.162nmol/L and 0.708nmol/L. The CDC method is currently used to assign "information only" values for 24,25(OH)2D3 in the quarterly distributions of the Vitamin D External Quality Assessment Scheme (DEQAS). Given the agreement (linear regression slope = 0.984, R2 = 0.996 and mean bias of -0.092nmol/L) between the CDC method and the NIST RMP observed in this study, the CDC-assigned 24,25(OH)2D3 values in DEQAS may provide a more accurate reference than the current participant consensus mean values.

With the advancement of artificial intelligence, the emulation of biological neural processes through neuromorphic computing has gained significant attention. Artificial optoelectronic synapses have emerged as promising components for neuromorphic computing due to their simple structure, low energy consumption, and ability to overcome the von Neumann bottleneck. Here, we design a multifunctional, energy-efficient optoelectronic synapse based on a formamidinium cesium lead iodide (FAxCs1-xPbI3)/Poly(3-hexylthiophene) (P3HT) heterojunction in a two-terminal vertical structure. The synaptic device exhibits key synaptic characteristics, such as excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and achieves a transition from short-term to long-term memory with an exceptionally low energy consumption of 0.59 fJ per synaptic event, and successfully emulates biological learning behavior, such as learning-forgetting-relearning. Long-term potentiation (LTP) enables efficient visual object recognition with 90.31% accuracy on the Modified National Institute of Standards and Technology (MNIST) data set using an artificial neural network (ANN). In addition, light logic functions ("AND", "OR") and associative learning (Pavlov's dog experiment) are demonstrated using 405 and 532 nm pulses. More significantly, optical wireless communication is experimentally performed using Morse code for words such as IITG, 2025, HELP, and SOS. Moreover, the device achieves 86.76% pixel-wise accuracy in the semantic segmentation of urban street scenes using a U-Net model. Finally, the working mechanism of the device, attributed to the efficient photogeneration of carriers and accumulation of electrons at the perovskite side, offers deep insights into the optoelectronic plasticity. These findings show the path toward the development of a highly integrated, photonic neuromorphic device for future intelligent systems.

As cyber threats and the misuse of personal data continue to increase, private browsing modes in web browsers such as Google Chrome and Mozilla Firefox are often perceived as solutions to enhance user privacy. However, these modes still leave traces of sensitive data in volatile memory (RAM), even though artifacts stored on disk-based storage are removed. This study evaluates the effectiveness of private browsing modes using the National Institute of Standards and Technology (NIST) framework integrated with Artificial Intelligence (AI) for forensic analysis. Simulation scenarios were conducted to assess the ability of private browsing modes to prevent data retention. The results indicate that although private browsing modes successfully eliminate disk-based traces, sensitive data such as account credentials can still be extracted from RAM. The integration of AI accelerates the detection of these artifacts. This research contributes to the field of digital forensics by providing a systematic framework for evaluating browser privacy mechanisms and offering insights for the development of real-time browser security tools.

The Standardization Readiness Level (SRL) in chip development serves as a critical metric for evaluating the transformation of technological achievements into industry standards. This study focuses on the 2026 "Summary Report on Chip Development Standardization Readiness Levels" (hereinafter referred to as the "NIST Report") published by the National Institute of Standards and Technology (NIST), systematically analyzing its SRL framework design logic, hierarchical definitions, and implementation processes. By comparing the supporting standard system of the EU's "European Chips Act" with the standardization framework of Japan's Ministry of Economy, Trade and Industry (METI) "Semiconductor Strategy 2.0," the study reveals similarities and differences in SRL practices across different institutional environments. Furthermore, a three-dimensional dynamic evolution model integrating "technology-industry-policy" dimensions is constructed to simulate SRL progression throughout the entire chip development lifecycle. Findings indicate that the NIST framework centers on "collaborative validation," achieving closed-loop management from concept to standardization through five-tiered hierarchical structures. The EU system emphasizes cross-border resource integration but faces limitations in decision-making efficiency, while the Japanese framework prioritizes corporate leadership but encounters international compatibility challenges. Dynamic modeling demonstrates that policy support and industrial collaboration serve as core drivers for SRL upgrades. This study provides theoretical references and practical guidance for optimizing standardization strategies among chip development stakeholders and enhancing standard systems for policymakers

Sepsis-associated acute kidney injury (SA-AKI) contributes to high morbidity and mortality rates in children; however, current diagnostic tools (serum creatinine and urine output) lack sensitivity for early detection. Metabolomics can be used to discover novel metabolic markers to enable early diagnosis and prognosis and provide therapeutic targets for SA-AKI. This study was aimed to identify the differentially expressed serum metabolites in children with SA-AKI. We conducted an untargeted serum metabolomic analysis using liquid chromatography-mass spectroscopy (LC-MS) in 75 children: 45 with sepsis and acute kidney injury (AKI) (15 each in Kidney Disease Improving Global Outcomes [KDIGO] stages 1-3), 15 with sepsis without AKI, and 15 healthy controls. Fasting blood samples were collected and centrifuged. Supernatant serum was stored at -80°C and subjected to untargeted metabolomic analysis using LC-MS. Reverse-phase and hydrophilic liquid interaction chromatographic separation was performed in positive and negative ion scan modes, while metabolite identification was performed using the SCIEX All-In-One HR-MS/MS Library with National Institute of Standards and Technology 2017 Library bundle. The significantly altered metabolites in AKI stage 3 were lipids belonging to the glycerophosphoethanolamine subgroup of the phospholipid class. Eighteen metabolites were differentially expressed in patients with SA-AKI versus healthy controls, with phosphoethanolamines (e.g., 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine) showing a consistent association with AKI stage. Thirty-one metabolites were significantly altered in severe AKI (KDIGO stages 2-3), of which only 4 could be annotated and increased proportionally with AKI severity. A pathway analysis revealed significant enrichment of glycerophospholipid, linoleic acid, and alpha-linolenic acid metabolism in stage 3 SA-AKI. Serum metabolomics is a feasible approach to identifying novel biomarkers of SA-AKI. Phosphoethanolamine metabolites both distinguished AKI from sepsis controls and were correlated with clinical severity (estimated glomerular filtration rate decline), suggesting its potential prognostic value. However, its diagnostic feasibility requires.

In today's internet era, marked by rapid advances in big data technology and the widespread use of intelligent robots, online education has become a central focus of educational research. This study employs deep learning, data mining techniques, and intelligent robot technology to construct an innovative convolutional neural network model for image acquisition and recognition. By optimizing weight initialization algorithms and activation functions, the model's performance has been enhanced to improve the accuracy and stability of image recognition. The results show that online education courses achieved 99.5% accuracy on the Modified National Institute of Standards and Technology dataset and 75.2% on the Canadian Institute for Advanced Research's CIFAR-10 dataset. Moreover, the activation functions tested reached recognition accuracies of 99.2% and 72.3%, respectively, under maximum iteration. Overall, the optimized CNN+ model has made significant strides in image recognition and can provide strong support for technological innovation in online education.

Abstract Brain-inspired neuromorphic computing systems require hardware components analogous to biological neurons and synapses. Honey based natural organic memristor has demonstrated promising nonvolatile memristive behaviors, with the advantages of sustainability, environmentally friendliness, and low-cost manufacturing. In this study, carbon nanotubes (CNTs) are added in honey to fabricate honey-CNT memristive artificial synaptic devices. Honey-CNT film is characterized by micro-Raman spectroscopy and the distribution of CNT bundles embedded in the honey-CNT composite layer by cross-sectional scanning electron microscopy for the first time. Critical synaptic functions of the honey-CNT memristor, including spike-rate-dependent plasticity, spike voltage dependent plasticity, learn-forget-relearn, and supralinear spatial summation are revealed, which have not been reported by honey based memristive devices before. Paired pulse facilitation with a PPF index as large as 4.5 is observed, indicating the enhancement of synaptic weight by CNTs. Furthermore, honey-CNT memristor based neuromorphic system is evaluated in terms of linearity, accuracy, read/write energy, and overall performance by the image recognition using Stochastic Gradient Descent and Adaptive Moment Estimation learning algorithms and the Modified National Institute of Standards and Technology database.

FAEST is a National Institute of Standards and Technology post-quantum signature candidate based on the Vector Oblivious Linear Evaluation-in-the-Head paradigm, whose signing performance is dominated by repeated Advanced Encryption Standard Counter-based Pseudorandom Generator calls. The reference implementation provides no FAEST-specialized acceleration for Advanced RISC Machine platforms. This paper proposes a three-layer Advanced Reduced Instruction Set Computer Machine NEON Single Instruction Multiple Data optimization: a register-resident 256-byte S-box with Table Lookup/Table Lookup with Extension-based SubBytes and four-way/eight-way parallel Advanced Encryption Standard processing; a fixed-length Pseudorandom Generator specialized for the FAEST tree structure; and Portable Operating System Interface for Unix thread-based parallelization of independent Vector Oblivious Linear Evaluation instances. Evaluated on all 12 parameter sets of FAEST v2 on Raspberry Pi 4 (without Advanced Reduced Instruction Set Computer Machine version 8 crypto-extensions) and Apple M2 (with hardware Advanced Encryption Standard support), the proposed method achieves signing speedups of up to 136.9x on Raspberry Pi 4 and 330.1x on Apple M2 over the pure-C reference. On Raspberry Pi 4, the NEON implementation outperforms OpenSSL; on Apple M2, the NEON-plus-Portable Operating System Interface for Unix thread configuration outperforms hardware-accelerated OpenSSL across all parameters, confirming that NEON SIMD combined with task-level parallelization can exceed hardware-accelerated single-thread throughput on Advanced Reduced Instruction Set Computer Machine-based platforms.

Accurate modeling of the volumetric behavior of ionic liquids (ILs) is crucial for guiding the design of electrolytes for energy storage and other chemical systems. While classical group contribution methods (GCMs) are grounded in thermodynamic theory, traditional machine learning (ML) models often lack physically consistent predictions and generalizability. To improve this, a hybrid modeling strategy is introduced that couples a reoptimized Classical-GCM with a physics-informed neural network (PINN-GCM), where thermodynamically optimized parameters from the Tait equation are directly incorporated into the hybrid loss function of the network. Building on the previous efforts of Jacquemin et al. (Ind. Eng. Chem. Res., 2017, 56, 6827-6840), the data set was extracted from the National Institute of Standards and Technology (NIST) database. The PINN-GCM framework was evaluated across 92 ILs, comprising 8,467 experimental data points spanning 217-473 K and 0.1-207 MPa. The aggregate performance yielded average RAAD values of 0.067 and 0.065% for the training and test sets, respectively, at the IL level. The ion-level models were trained on 6,049 points from 59 ILs (32 cations and 28 anions), with extrapolation evaluated on 2,958 points from 21 unseen IL combinations, demonstrating strong combinatorial generalization to new pairings of known ions, although structural generalization to entirely novel ion chemistries remains beyond the scope of the current model. The framework shows promise for integration into process simulation tools and extension to related IL properties (viscosity and conductivity), although its applicability is validated within the experimental temperature-pressure range and requires ions present in the established library. This strategy highlights the potential of merging physics-based modeling and ML to develop foundational models for multiproperty prediction, thereby promoting the improved design of safer electrolytes and other chemical systems in the future.