Seed Dormancy Class and Germination Characteristics of Prunus spachiana (Lavallée ex Ed.Otto) Kitam. f. ascendens (Makino) Kitam Native to the Korean Peninsula

Prunus spachiana (Lavallée ex Ed.Otto) Kitam. f. ascendens (Makino) Kitam leaves exert natural anti-inflammatory effects by inhibiting nitric oxide formation. P. spachiana flowers bloom earlier than other Prunus spp. and thus could serve as a valuable resource for the horticulture and pharmaceutical industries. However, its seed dormancy class and germination traits remain uncharacterized. Thus, this study aimed to characterize the seed dormancy and germination of P. spachiana. Imbibition, phenological, and move-along experiments were performed, and the effects of H2SO4 treatment, hormone soaking, warm/cold stratification, and endocarp removal on germination were explored. Observation revealed that ripe seeds of P. spachiana contain developed embryos and are water permeable. Radicle and shoot emergence began in March and April, respectively, under natural conditions in the year following production. No seed germination was observed after 30 days of incubation at 4, 15/6, 20/10, or 25/15 °C under light/dark conditions, indicating the physiological dormancy of the seeds. Germination increased with prolonged stratification and was affected by incubation temperature. Seed scarification by H2SO4 and soaking with gibberellic acid (GA3) and fluridone were ineffective in breaking dormancy. However, GA3 soaking of the seeds after endocarp removal effectively induced germination (100%). These results indicate that P. spachiana seeds exhibit intermediate physiological dormancy.

New Insights on the Formation of Nucleation Mode Particles in a Coastal City Based on a Machine Learning Approach

Atmospheric particles have profound implications for the global climate and human health. Among them, ultrafine particles dominate in terms of the number concentration and exhibit enhanced toxic effects as a result of their large total surface area. Therefore, understanding the driving factors behind ultrafine particle behavior is crucial. Machine learning (ML) provides a promising approach for handling complex relationships. In this study, three ML models were constructed on the basis of field observations to simulate the particle number concentration of nucleation mode (PNCN). All three models exhibited robust PNCN reproduction (R2 > 0.80), with the random forest (RF) model excelling on the test data (R2 = 0.89). Multiple methods of feature importance analysis revealed that ultraviolet (UV), H2SO4, low-volatility oxygenated organic molecules (LOOMs), temperature, and O3 were the primary factors influencing PNCN. Bivariate partial dependency plots (PDPs) indicated that during nighttime and overcast conditions, the presence of H2SO4 and LOOMs may play a crucial role in influencing PNCN. Additionally, integrating additional detailed information related to emissions or meteorology would further enhance the model performance. This pilot study shows that ML can be a novel approach for simulating atmospheric pollutants and contributes to a better understanding of the formation and growth mechanisms of nucleation mode particles.

Optimisation of Urine Sample Preparation for Headspace-Solid Phase Microextraction Gas Chromatography-Mass Spectrometry: Altering Sample pH, Sulphuric Acid Concentration and Phase Ratio

Headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) can be used to measure volatile organic compounds (VOCs) in human urine. However, there is no widely adopted standardised protocol for the preparation of urine samples for analysis resulting in an inability to compare studies reliably between laboratories. This paper investigated the effect of altering urine sample pH, volume, and vial size for optimising detection of VOCs when using HS-SPME-GC-MS. This is the first, direct comparison of H₂SO₄, HCl, and NaOH as treatment techniques prior to HS-SPME-GC-MS analysis. Altering urine sample pH indicates that H₂SO₄ is more effective at optimising detection of VOCs than HCl or NaOH. H₂SO₄ resulted in a significantly larger mean number of VOCs being identified per sample (on average, 33.5 VOCs to 24.3 in HCl or 12.2 in NaOH treated urine) and more unique VOCs, produced a more diverse range of classes of VOCs, and led to less HS-SPME-GC-MS degradation. We propose that adding 0.2 mL of 2.5 M H₂SO₄ to 1 mL of urine within a 10 mL headspace vial is the optimal sample preparation prior to HS-SPME-GC-MS analysis. We hope the use of our optimised method for urinary HS-SPME-GC-MS analysis will enhance our understanding of human disease and bolster metabolic biomarker identification.

Quantum chemical and experimental evaluation of the inhibitory action of two imidazole derivatives on mild steel corrosion in sulphuric acid medium

The adsorption and corrosion inhibition properties of two imidazole derivatives namely, 2-(4-chlorophenyl)-1,4,5-triphenyl-1H-imidazole (IM-Cl) and 1,4,5-triphenyl-2-(p-tolyl)-1H-imidazole (IM-CH₃) for mild steel in 0.5 M H₂SO₄ solution are studiedby electrochemical and computational calculations. The results obtained from the electrochemical methods show that IM-Cl and IM-CH₃imparted high resistance and behave as mixed type inhibitors. Inhibition efficiency (IE %) increases with the increase of inhibitors concentration to attain 96 % and 91% at 10⁻³ M of IM-Cl and IM-CH₃ respectively. EISdatais analyzed to model the inhibition process through appropriate equivalent circuit model. Thermodynamic and kinetic parameters controlling the adsorption process are calculated and discussed. DFT calculations are carried out at the B3LYP levels of theory with 6-31G (d,p) basis stein gas and aqueous phase for neutral and protonated forms. Quantum chemical calculations section of the study provides enough calculation and discussion on the relationship between corrosion inhibition and global reactivity descriptors.