Nanomaterial-enhanced analytical methods have emerged as transformative platforms in forensic science, addressing critical sensitivity, selectivity, and operational limitations of conventional techniques across trace evidence, toxicology, biological fluid analysis, and nucleic acid profiling. Gold nanoparticles, quantum dots, carbon-based nanomaterials, and magnetic nanoparticles have collectively enabled detection capabilities at femtomolar to attomolar concentrations, multiplexed immunoassay formats, magnetically assisted sample preparation from degraded biological matrices, and enhanced PCR amplification from inhibitor-rich forensic specimens. Surface-enhanced Raman scattering, fluorescence-based transduction, and electrochemical sensing at nanocomposite electrode surfaces have each demonstrated performance profiles that substantially exceed conventional forensic analytical benchmarks. Despite these advances, the translation of nanomaterial platforms into accredited forensic casework remains constrained by nanoparticle aggregation instability, batch-to-batch synthesis variability, matrix-dependent signal suppression, and the absence of universally adopted validation frameworks governing limit of detection determination, measurement uncertainty quantification, and proficiency testing for nanomaterial-specific analytical modalities. Emerging innovations including portable handheld SERS devices, blockchain-integrated chain-of-custody architectures, molecularly imprinted polymer nanoparticle probes, and AI-augmented chemometric classification frameworks are collectively advancing the field toward real-time, field-deployable forensic analysis. Sustained progress requires parallel investment in international standardization, ethical governance of ultra-sensitive biological surveillance capabilities, and equitable access infrastructure ensuring that nanomaterial-enabled forensic precision serves justice systems globally.
This study introduces an enhanced anomaly detection framework integrating Time-[Formula: see text]-Variational Autoencoders (Time-[Formula: see text]-VAE) and Transformer architectures for blockchain-based carbon trading markets. Against intensifying global climate challenges, ensuring carbon market integrity is critical. While blockchain technology enhances transparency, it simultaneously introduces novel regulatory complexities in detecting sophisticated anomalies. Our improved hybrid model, trained on raw transaction records of Moss Carbon Credit (MCO2) tokens sourced via Ethereum blockchain APIs, demonstrates significant efficacy in identifying critical anomalies including smart contract-driven token distribution and fake liquidity attacks through empirical case analysis. The research establishes a scientific framework for blockchain deployment and supervision in carbon markets.
Archana B, Adithya Baragi S, K. N. Anusha, Jeevan Basri B S Β· 5 authors
Evidence management is crucial in the field of forensic science. Evidence obtained from a crime scene is important in solving the case and delivering justice to the victim involved. Hence, protecting the integrity of the evidence throughout the process is of prime importance. Chain of Custody (CoC) is the process which maintains the integrity of the evidence using Blockchain Technology. Inability to maintain the chain of custody will make the evidence inadmissible in court, eventually leading to the case dismissal. Digitalization of forensic evidence management system is a need of time as it is an environment friendly model. Blockchain are digitally distributed ledgers of transactions signed cryptographically in chronological order that are sorted into blocks and is completely open to anyone in the blockchain network. Present study aims to create a framework and further propose an algorithm to implement blockchain technology to digitalize forensic evidence management system and maintain Chain of Custody
Sarah Khadijah Taylor, Steve Ho-yong Kim, Khairul Akram Zainol Ariffin, Siti Norul Huda Sheikh Abdullah
Studies have shown that the existing methodology of digital forensics preservation, which is to acquire and hash the evidence, is insufficient for cryptocurrencies as it does not secure the value. To address this issue, investigators secure the cryptocurrency by transferring it to a crypto wallet controlled by the Law Enforcement Agencies(LEAs). This process will unavoidably modify some data. Despite the criticality of this issue, inadequate studies have been made in this area. In addition, current guidelines on securing the cryptocurrency lack a comprehensive description from the perspective of digital evidence preservation principles. Crucial data to be documented throughout the preservation process were also not properly listed. Therefore, this study aims to address the gap in preserving cryptocurrencies from crypto wallets. Three objectives were then laid out; (1) to develop a methodology that is mapped comprehensively with digital evidence preservation principle, (2) to describe and provide justification on the inevitably modified data, and (3) to list crucial data to be documented during preservation process. The methods to achieve the objectives were critical examinations on various types of crypto wallets and by using simulation. The result shows that the study is able to provide a comprehensive crypto wallets preservation methodology to forensic investigators. It is hoped that the outcome from this study will promote better understanding, ensure consistency of implementation, and to aid investigators in explaining and justifying their actions during search and seizure in court.