Liquid scintillator detectors have great advantages in the field of radionuclide detection because of their low detection limit, high sensitivity, and diverse functions. However, the material properties of liquid scintillators directly determine their detection effectiveness, which leads to their poor vertex resolution and particle identification. In this work, we introduce the improvement methods, choices, and properties of different novel liquid scintillator materials in recent years. This article is expected to provide references for the development and research of liquid scintillator materials in various application fields.

With the popularization of nuclear energy in the field of energy application, the effective removal of radioactive iodine isotopes is crucial for the long-term development of nuclear energy. In this paper, bimetallic MOFs with different Cu/Bi ratios were synthesized by a simple solvothermal method, and a bimetallic nano-adsorbent Cu_x/Bi_10−x-NC was prepared by one-step calcination. Adsorption experiments show that Cu_x/Bi_10−x-NC exhibits excellent adsorption performance for iodide ions, gaseous iodine, and I₂ in cyclohexane solution, with the maximum adsorption capacities reaching up to 484.08 and 233.11 mg g⁻¹, respectively. Through the characterization of the material system before and after adsorption, this excellent adsorption performance is attributed to the synergistic effect between Cu and Bi, as well as the highly dispersed adsorption active sites derived from the MOF template. Therefore, the prepared Cu_x/Bi_10−x-NC has great potential in the efficient and stable capture of various forms of iodine.

Alpha/beta (α/β) pulse shape discrimination (PSD) in liquid scintillation counting (LSC) is fundamentally limited by the charge comparison method (CCM) at low energies, where the entire tritium (³H) beta spectrum resides (0–18.6 keVee). The CCM figure-of-merit drops below 0.6 in this region, rendering it inadequate for simultaneous tritium and natural uranium alpha monitoring in nuclear power plant (NPP) liquid effluents. We present a one-dimensional convolutional neural network (1D-CNN) trained on an 80,000-waveform physics-based simulation dataset using established scintillation parameters for Ultima Gold AB. The proposed network achieves 97.4% overall classification accuracy and an area under the receiver operating characteristic curve (AUC) of 0.9981 on the held-out test set, representing improvements of 13.8 percentage points and 0.046 AUC over CCM. In the critical 0–18.6 keVee region, CNN accuracy exceeds 95% compared to below 60% for CCM—a greater than 35 percentage point improvement. Pulse amplitude discrimination (PAD), evaluated as a preliminary screening method, exhibits a 6.3% alpha spillover rate into the beta window, exceeding the regulatory limit of 3%. Gradient-weighted class activation maps (Grad-CAM) confirm that the network exploits physically meaningful pulse features rather than simulation artefacts. A comprehensive background suppression strategy combining dual-SiPM coincidence (24× reduction), anti-coincidence guard detector (5.8× reduction), composite passive shielding (10× reduction), and CNN-assisted discrimination reduces the system equivalent background to 1.83 ± 0.12 cpm, yielding a tritium minimum detectable activity (MDA) of 0.21 Bq/mL (10 mL sample, 30 min count), which satisfies the GB 14587 reference limit of 0.5 Bq/mL. After 8-bit post-training quantisation, the model achieves sub-microsecond inference latency on an embedded Xilinx Artix-7 Field-programmable gate array(FPGA), enabling real-time deployment in portable online monitoring systems.

Real-time monitoring of radioactive liquid effluents from nuclear power plants (NPPs) is essential for environmental safety assurance, yet existing liquid scintillation counting (LSC) instruments are bulky (>200 kg), laboratory-bound, and incapable of autonomous online deployment. This paper presents the design and implementation of a fully automated online LSC monitoring process integrating seawater sampling, distillation pre-treatment, liquid scintillator mixing, dual photomultiplier tube (PMT) coincidence detection, field-programmable gate array (FPGA)-based digital signal processing, and 4G remote data transmission in a single portable unit weighing 21.59 kg. The automated process executes a complete sample-to-result cycle in approximately 45 min without human intervention. The signal processing chain comprises a dual-PMT coincidence system, a custom two-stage pre-amplifier, a 14-bit 40 MSPS analogue-to-digital converter (ADC; AD9245, Analog Devices, Norwood, MA, USA), and a five-stage FPGA pipeline implementing anti-coincidence rejection, pulse amplitude discrimination, charge comparison method (CCM) waveform discrimination, and convolutional neural network (CNN)-based alpha/beta classification achieving 97.4% accuracy on a Geant4-simulated test set. System performance was validated against a PerkinElmer 1220 QUANTULUS reference spectrometer across a five-point calibration range (0–400 Bq/L; R² = 0.9987, recovery 99.4–101.6%), confirmed via third-party environmental testing (−10 °C to +50 °C, GB/T 2423.1-2008), and verified in field measurements at Tianwan Nuclear Power Plant. The experimentally determined system background is (1.83 ± 0.12) cpm; the calculated minimum detectable activity (MDA) for tritium is 0.073 Bq/mL at 30 min counting time (η = 3.4%, V = 10 mL, T_s = 1800 s per the Currie formulation), satisfying the GB 14587 (the Chinese national standard: Limits of Radioactivity for Liquid Effluents from Nuclear Power Plant) regulatory reference limit of 0.5 Bq/mL with a 7× safety margin. The proposed system is, to the authors’ knowledge, the first reported instrument combining full process automation (including distillation pre-treatment), single-person portability, and real-time 4G remote data transmission for continuous NPP liquid effluent surveillance in high-salinity seawater environments.