Novel Potassium Polynitrides at High Pressures

Polynitrogen compounds have attracted great interest due to their potential applications as high energy density materials. Most recently, a rich variety of alkali polynitrogens (R_{x}N_{y}; R=Li, Na, and Cs) have been predicted to be stable at high pressures and one of them, CsN_{5} has been recently synthesized. In this work, various potassium polynitrides are investigated using first-principles crystal structure search methods. Several novel molecular crystals consisting of N_{4} chains, N_{5} rings, and N_{6} rings stable at high pressures are discovered. In addition, an unusual nitrogen-rich metallic crystal with stoichiometry K_{2}N_{16} consisting of a planar two-dimensional extended network of nitrogen atoms arranged in fused eighteen atom rings is found to be stable above 70 GPa. An appreciable electron transfer from K to N atoms is responsible for the appearance of unexpected chemical bonding in these crystals. The thermodynamic stability and high pressure phase diagram is constructed. The electronic and vibrational properties of the layered polynitrogen K_{2}N_{16} compound are investigated, and the pressure-dependent IR-spectrum is obtained to assist in experimental discovery of this new high-nitrogen content material

Hardware security design from circuits to systems

The security of hardware implementations is of considerable importance, as even the most secure and carefully analyzed algorithms and protocols can be vulnerable in their hardware realization. For instance, numerous successful attacks have been presented against the Advanced Encryption Standard, which is approved for top secret information by the National Security Agency. There are numerous challenges for hardware security, ranging from critical power and resource constraints in sensor networks to scalability and automation for large Internet of Things (IoT) applications. The physically unclonable function (PUF) is a promising building block for hardware security, as it exposes a device-unique challenge-response behavior which depends on process variations in fabrication. It can be used in a variety of applications including random number generation, authentication, fingerprinting, and encryption. The primary concerns for PUF are reliability in presence of environmental variations, area and power overhead, and process-dependent randomness of the challenge-response behavior. Carbon nanotube field-effect transistors (CNFETs) have been shown to have excellent electrical and unique physical characteristics. They are a promising candidate to replace silicon transistors in future very large scale integration (VLSI) designs. We present the Carbon Nanotube PUF (CNPUF), which is the first PUF design that takes advantage of unique CNFET characteristics. CNPUF achieves higher reliability against environmental variations and increases the resistance against modeling attacks. Furthermore, CNPUF has a considerable power and energy reduction in comparison to previous ultra-low power PUF designs of 89.6% and 98%, respectively. Moreover, CNPUF allows a power-security tradeoff in an extended design, which can greatly increase the resilience against modeling attacks. Despite increasing focus on defenses against physical attacks, consistent security oriented design of embedded systems remains a challenge, as most formalizations and security models are concerned with isolated physical components or a high-level concept. Therefore, we build on existing work on hardware security and provide four contributions to system-oriented physical defense: (i) A system-level security model to overcome the chasm between secure components and requirements of high-level protocols; this enables synergy between component-oriented security formalizations and theoretically proven protocols. (ii) An analysis of current practices in PUF protocols using the proposed system-level security model; we identify significant issues and expose assumptions that require costly security techniques. (iii) A System-of-PUF (SoP) that utilizes the large PUF design-space to achieve security requirements with minimal resource utilization; SoP requires 64% less gate-equivalent units than recently published schemes. (iv) A multilevel authentication protocol based on SoP which is validated using our system-level security model and which overcomes current vulnerabilities. Furthermore, this protocol offers breach recognition and recovery. Unpredictability and reliability are core requirements of PUFs: unpredictability implies that an adversary cannot sufficiently predict future responses from previous observations. Reliability is important as it increases the reproducibility of PUF responses and hence allows validation of expected responses. However, advanced machine-learning algorithms have been shown to be a significant threat to the practical validity of PUFs, as they can accurately model PUF behavior. The most effective technique was shown to be the XOR-based combination of multiple PUFs, but as this approach drastically reduces reliability, it does not scale well against software-based machine-learning attacks. We analyze threats to PUF security and propose PolyPUF, a scalable and secure architecture to introduce polymorphic PUF behavior. This architecture significantly increases model-building resistivity while maintaining reliability. An extensive experimental evaluation and comparison demonstrate that the PolyPUF architecture can secure various PUF configurations and is the only evaluated approach to withstand highly complex neural network machine-learning attacks. Furthermore, we show that PolyPUF consumes less energy and has less implementation overhead in comparison to lightweight reference architectures. Emerging technologies such as the Internet of Things (IoT) heavily rely on hardware security for data and privacy protection. The outsourcing of integrated circuit (IC) fabrication introduces diverse threat vectors with different characteristics, such that the security of each device has unique focal points. Hardware Trojan horses (HTH) are a significant threat for IoT devices as they process security critical information with limited resources. HTH for information leakage are particularly difficult to detect as they have minimal footprint. Moreover, constantly increasing integration complexity requires automatic synthesis to maintain the pace of innovation. We introduce the first high-level synthesis (HLS) flow that produces a threat-targeted and security enhanced hardware design to prevent HTH injection by a malicious foundry. Through analysis of entropy loss and criticality decay, the presented algorithms implement highly resource-efficient targeted information dispersion. An obfuscation flow is introduced to camouflage the effects of dispersion and reduce the effectiveness of reverse engineering. A new metric for the combined security of the device is proposed, and dispersion and obfuscation are co-optimized to target user-supplied threat parameters under resource constraints. The flow is evaluated on existing HLS benchmarks and a new IoT-specific benchmark, and shows significant resource savings as well as adaptability. The IoT and cloud computing rely on strong confidence in security of confidential or highly privacy sensitive data. As (differential) power attacks can take advantage of side-channel leakage to expose device-internal secrets, side-channel leakage is a major concern with ongoing research focus. However, countermeasures typically require expert-level security knowledge for efficient application, which limits adaptation in the highly competitive and time-constrained IoT field. We address this need by presenting the first HLS flow with primary focus on side-channel leakage reduction. Minimal security annotation to the high-level C-code is sufficient to perform automatic analysis of security critical operations with corresponding insertion of countermeasures. Additionally, imbalanced branches are detected and corrected. For practicality, the flow can meet both resource and information leakage constraints. The presented flow is extensively evaluated on established HLS benchmarks and a general IoT benchmark. Under identical resource constraints, leakage is reduced between 32% and 72% compared to the baseline. Under leakage target, the constraints are achieved with 31% to 81% less resource overhead

Polymeric nitrogen by plasma enhanced chemical vapor deposition

With the urgent need for new environmentally-friendly energetic materials, the field of polymeric nitrogen, predicted to be a high energy density energetic, is now at a critical stage in its development. In spite of extensive first principles calculations regarding the existence and stability of different polymeric nitrogen structures, their successful syntheses have been rare. This dissertation describes the first detailed study of a plasma-enhanced chemical vapor deposition (PECVD) approach to the synthesis of polymeric nitrogen. PECVD provides non-equilibrium conditions known to produce high pressure-temperature phases. Molecular nitrogen mixed with hydrogen and argon is used as the gas phase precursor to provide nitrogen and passivating hydrogen species. In addition, either solid sodium and lithium azide or azide solution infiltrated sheets of carbon nanotube substrates have been used to initiate plasma polymerization to a polymeric nitrogen phase. Characterization of the samples produced were conducted using micro-Raman spectroscopy, attenuated total reflectance-Fourier transform infrared spectroscopy, powder X-ray diffraction, and temperature programmed desorption. Sample morphologies and compositions have also been performed using scanning electron microscopy combined with energy- dispersive X-ray analysis. The results show that a mixture of polymeric nitrogen phases is formed that is stable under ambient conditions and decompose near 400°C. The long-sought-after cubic-gauche polymeric nitrogen (cg-PN) phase, produced only in a diamond anvil cell at high pressure high temperature conditions and not recoverable under ambient conditions, is shown by the powder diffraction data to be one of the polymeric nitrogen phases synthesized by the plasma process. Density Functional Theory (DFT) calculations were also used to investigate the metastability of cg-PN and that of related nitrogen clusters at ambient conditions in order to understand some of the results. Although these phases were obtained with and without carbon nanotube substrates, the spectroscopic results suggest that carbon nanotubes play a significant role in faster and more efficient plasma synthesis possibly due to stabilization of a PN phase inside the walls of carbon nanotubes. The effect of carbon nanotubes on polymeric nitrogen growth will be investigated by transmission electron microscopy in future studies

Fate and effects of graphene oxide alone and with sorbed benzo(a)pyrene in mussels Mytilus galloprovincialis

Graphene oxide (GO) has gained a great scientific and economic interest due to its unique properties. As incorporation of GO in consumer products is rising, it is expected that GO will end up in oceans. Due to its high surface to volume ratio, GO can adsorb persistent organic pollutants (POPs), such as benzo(a)pyrene (BaP), and act as carrier of POPs, increasing their bioavailability to marine organisms. Thus, uptake and effects of GO in marine biota represent a major concern. This work aimed to assess the potential hazards of GO, alone or with sorbed BaP (GO+BaP), and BaP alone in marine mussels after 7 days of exposure. GO was detected through Raman spectroscopy in the lumen of the digestive tract and in feces of mussels exposed to GO and GO+BaP while BaP was bioaccumulated in mussels exposed to GO+BaP, but especially in those exposed to BaP. Overall, GO acted as a carrier of BaP to mussels but GO appeared to protect mussels towards BaP accumulation. Some effects observed in mussels exposed to GO+BaP were due to BaP carried onto GO nanoplatelets. Enhanced toxicity of GO+BaP with respect to GO and/or BaP or to controls were identified for other biological responses, demonstrating the complexity of interactions between GO and BaP.This work was funded by the Spanish MINECO (NACE project CTM2016–81130-R) and the Basque Government (grants to consolidated research group IT1302–19 and IT1743–22, and predoctoral fellowship to NGS)

Small Groups, Big Weapons: The Nexus of Emerging Technologies and Weapons of Mass Destruction Terrorism

Historically, only nation-states have had the capacity and resources to develop weapons of mass destruction (WMD). This was due to the significant capital, infrastructure, and intellectual capacity required to develop and maintain a WMD program. This paradigm, however, is shifting. To be clear, non-state actors have been interested in WMD for decades. In fact, over a 26-year period, there were 525 incidents by non-state actors involving nuclear, biological, and chemical agents. But the scale of these incidents was relatively low level when compared to the impact of terrorist attacks using conventional weapons. However, this reality must be reexamined given the commercialization of emerging technologies that is reducing the financial, intellectual, and material barriers required for WMD development and employment. This report serves as a primer that surveys the key challenges facing non-state actors pursuing WMD capabilities, and the potential for certain emerging technologies to help overcome them. While there are numerous examples of such technologies, this report focuses on synthetic biology, additive manufacturing (AM) (commonly known as 3D printing), and unmanned aerial systems (UAS). There is a wide range of expert opinions regarding the dual-use nature of the technologies discussed in this report, the ease of their possible misuse, and the potential threats they pose. The varied opinions of scientists and government officials highlight the challenges these technologies pose to developing a cohesive strategy to prevent their proliferation for nefarious use by non-state actors. Much of the risk and threat associated with these dual-use technologies resides in the intent of the user

A Survey of hardware protection of design data for integrated circuits and intellectual properties

International audienceThis paper reviews the current situation regarding design protection in the microelectronics industry. Over the past ten years, the designers of integrated circuits and intellectual properties have faced increasing threats including counterfeiting, reverse-engineering and theft. This is now a critical issue for the microelectronics industry, mainly for fabless designers and intellectual properties designers. Coupled with increasing pressure to decrease the cost and increase the performance of integrated circuits, the design of a secure, efficient, lightweight protection scheme for design data is a serious challenge for the hardware security community. However, several published works propose different ways to protect design data including functional locking, hardware obfuscation, and IC/IP identification. This paper presents a survey of academic research on the protection of design data. It concludes with the need to design an efficient protection scheme based on several properties

Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection

Deep neural networks (DNNs) have demonstrated their superiority in practice. Arguably, the rapid development of DNNs is largely benefited from high-quality (open-sourced) datasets, based on which researchers and developers can easily evaluate and improve their learning methods. Since the data collection is usually time-consuming or even expensive, how to protect their copyrights is of great significance and worth further exploration. In this paper, we revisit dataset ownership verification. We find that existing verification methods introduced new security risks in DNNs trained on the protected dataset, due to the targeted nature of poison-only backdoor watermarks. To alleviate this problem, in this work, we explore the untargeted backdoor watermarking scheme, where the abnormal model behaviors are not deterministic. Specifically, we introduce two dispersibilities and prove their correlation, based on which we design the untargeted backdoor watermark under both poisoned-label and clean-label settings. We also discuss how to use the proposed untargeted backdoor watermark for dataset ownership verification. Experiments on benchmark datasets verify the effectiveness of our methods and their resistance to existing backdoor defenses. Our codes are available at \url{https://github.com/THUYimingLi/Untargeted_Backdoor_Watermark}.Comment: This work is accepted by the NeurIPS 2022 (Oral, TOP 2%). The first two authors contributed equally to this work. 25 pages. We have fixed some typos in the previous versio

The influence of Arsenic on the toxicity of carbon nanoparticles in bivalves

Although an increasing number of studies have been published on the effects of emergent pollutants such as carbon nanoparticles, there is still scarce information on the impact of these contaminants on marine organisms when acting in combination with classical pollutants such as meta(loid)s. The present study evaluated the impacts of Arsenic and Multi-Walled Carbon Nanotubes (MWCNTs) in the clam Ruditapes philippinarum, assessing the effects induced when both contaminants were acting individually (As, NP) and as a mixture (As+NP). Metabolic capacity (electron transport system activity), oxidative stress (antioxidant and biotransformation enzymes activity and cellular damage) and neurotoxicity (Acetylcholinesterase activity) biomarkers were evaluated inclams aftera28 daysexposure period.Theresults obtained showedthatthe accumulation ofAs was not affected by the presence of the NPs. Our results demonstrated that higher injuries were noticed in clams exposed to NPs, with higher metabolic depression and oxidative stress, regardless of the presence of As. Furthermore, higher neurotoxicity was observed in clams exposed to the combination of both contaminants in comparison to the effects of As and NPs individually.publishe