Trace lead ions (Pb2+) can seriously damage ecosystems and human health. Thus, it is urgent to develop reliable detection methods with high sensitivity and selectivity. This work constructs an electrochemical/colorimetric dual-mode biosensor for ultra-trace Pb2+ detection. The sensor combines Pb2+-specific DNAzyme recognition with enzyme-free catalytic hairpin assembly-hybrid chain reaction (CHA-HCR) cascade signal amplification technology. The experimental results indicate that this dual-mode sensing platform exhibits exceptional detection performance. The limit of detection (LOD) reaches 1.76 pM (electrochemical mode) and 32.17 pM (colorimetric mode). The corresponding linear ranges are 0.005 - 5000 nM and 0.1 - 10000 nM respectively. Moreover, the sensor demonstrates high selectivity against interfering metal ions. It maintains good storage stability, retaining over 84 % of its initial activity after being stored at 4 °C for 3 weeks. It also achieves satisfactory detection reliability for real water samples collected from the Pinglu Canal, with recovery rates ranging from 97.5 % to 99.3 %. This work provides a novel, sensitive, and robust technical solution for aquatic Pb2+ monitoring. It shows great application potential for on-site environmental detection and public health protection.
Yong-ping Gao, Chuanyang Huang, Xinwei Bai et al.· Biosensors & bioelectronics· 0 citations
Smart contract failures can cause irreversible financial and operational losses, yet current validation workflows still rely heavily on warnings, execution traces, and manually authored tests that are difficult to scale for contract-specific logic. Existing automated tools—static analyzers, symbolic executors, and fuzzers—primarily produce issue-centered outputs such as warnings or counterexamples rather than structured, reviewable test artifacts. This paper investigates whether large language models (LLMs) can bridge this gap by generating structured draft test specifications from Solidity source code. We propose AutoTestAI, an exploratory implementation-oriented framework that combines contract preprocessing, a structured auditor-style prompt, and multilayer output purification to produce machine-readable CSV test specifications suitable for downstream review and possible test implementation. On a 27-contract main benchmark, AutoTestAI achieves 91.3% overall function-level coverage. Under the revised baseline-and-ablation framework, CSV purification improves mean contract-level coverage on the Main27 split from 0.00% (strong_raw) to 91.53% (strong_purified), while enabling preprocessing within the full pipeline improves Holdout40 mean coverage from 69.73% (autotestai_no_preprocess) to 83.46% (autotestai_full), with both coverage gains statistically significant under paired Wilcoxon testing ( $p{\lt }0.01$ ). An expanded Foundry executability-convertibility validation shows 141/141 converted tests compiled and 133/141 executed successfully. A fresh 40-contract multi-project holdout set and a model sensitivity check with gpt-4o further examine broader applicability and model dependence. The method is intended as a practical complementary layer alongside static analysis, symbolic execution, fuzzing, and expert review, rather than a production-ready auditing framework or a substitute for formal verification, exhaustive testing, or expert judgment.
Shengyu Xie, Xingxing Yang, Yun Pan et al.· IEEE Access· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
