Abstract Formalin is widely used in educational facilities for specimen preservation and laboratory experiments; however, its primary component, formaldehyde, is a volatile, toxic, and carcinogenic substance that may pose significant health risks when released indoors. This study experimentally evaluated sodium bicarbonate (NaHCO3), urea (H2NCONH2), and sodium hydroxide (NaOH) as candidate materials for a low-hazard source-control strategy for formalin spill incidents in educational facilities. Qualitative bromothymol blue (BTB) indicator assessments and GC–MS analyses showed that sodium bicarbonate and sodium hydroxide substantially reduced formaldehyde-associated responses, whereas urea exhibited little observable effect under the conditions tested. In sealed-chamber experiments simulating indoor formalin spill scenarios, airborne formaldehyde concentrations increased rapidly and exceeded 800 ppm under untreated conditions. In contrast, sodium bicarbonate treatment maintained concentrations below 20 ppm throughout the experimental period and effectively suppressed concentration buildup over time. These findings suggest that sodium bicarbonate may reduce airborne formaldehyde release by altering physicochemical conditions that influence formaldehyde volatilization. Owing to its low hazard profile, favorable handling characteristics, wide availability, and operational practicality, sodium bicarbonate demonstrated considerable potential as a low-hazard source-control material capable of complementing conventional ventilation- and dilution-based Hazmat response strategies for indoor formalin spill incidents.
Abstract Base baths, which typically employ strong bases dissolved in alcohols, are frequently used to clean laboratory glassware but present significant fire, chemical, and handling hazards when improperly prepared or maintained. This work evaluates prior awareness, documentation practices, and daily use of base baths within an academic research environment. It seeks to improve safety education on users’ knowledge and behaviors. Initial surveys revealed knowledge gaps regarding base bath fire risks, the correct use of personal protective equipment (PPE), proper secondary containment measures, and improper labeling and documentation practices. In response, standardized training materials, safety briefings, and a formal standard operating procedure were developed and distributed through departmental seminars and interactive training sessions. Follow-up surveys demonstrate improved recognition of proper base bath preparation, increased awareness of appropriate spill/splash response, more precise identification of associated fire hazards, and greater confidence in working with base baths. The reported use of labeling, lids, secondary containment, and appropriate PPE increased following these safety interventions. Collectively, these results indicate that structured, low-barrier safety programming combined with accessible written guidance measurably improved hazard recognition and risk mitigation during handling base baths. This framework provides a practical model for strengthening the chemical safety culture through student-led initiatives focused on high-risk cleaning practices, which are standard in research laboratories and may be adapted to other commonly overlooked laboratory hazards.
Abstract Five machine learning models were trained using accident reports from the National Institute of Chemical Safety (NICS) that occurred in the Republic of Korea from 2014 to 2025 to classify the accidents’ causes. Even though some causes were difficult to distinguish because they were described using similar language in the accident reports, the models performed reasonably well with F1 scores of 0.7608, 0.8086, 0.7687, 0.8070, and 0.8215 and Area Under the Precision-Recall Curve (AUPRC) scores of 0.8284, 0.8354, 0.8548, 0.8434, and 0.8807, respectively, for Logistic Regression (LR), Random Forest (RF), Supporting Vector Machine (SVM), XGBoost, and Korean Bidirectional Encoder Representations from Transformers (KoBERT). A Local Interpretable Model-agnostic Explanations (LIME) XAI (explainable artificial intelligence) method was applied to analyze the decision-making process, which revealed symmetrical keyword reliance that reflects the inherent conceptual overlap between the two primary causes as defined by the NICS: “facility defect” and “noncompliance with safety standards”. Our results provide recommendations to overcome the inherent limitations of the NICS data set and highlight the necessity of a fine-grained classification framework using XAI approaches.
Abstract The emergence of nitrosamine drug-substance-related impurities (NDSRIs) has presented a complex and evolving challenge to pharmaceutical manufacturing and regulatory oversight. Unlike traditional nitrosamines, NDSRIs may form through multiple pathways involving active pharmaceutical ingredients (APIs), synthetic intermediates, excipients, packaging components, and degradation processes. This review integrates the current understanding of NDSRI formation with implications for pharmaceutical process development, impurity control, and lifecycle risk mitigation. Key structural alerts, including secondary and tertiary amines, piperazines, and hydrazines, are discussed in relation to nitrosating agents, oxidative stressors, and solid-state properties. Special attention is given to precursor-derived and degradation-induced nitrosation, polymorphic variability, and moisture sensitivity, all of which influence NDSRI formation during both the synthesis and formulation stages. Case examples in high-risk therapeutic classes (e.g., antihypertensive drugs, antimicrobial agents, and antidiabetic agents) illustrate diverse routes of NDSRI formation and control opportunities. Mitigation strategies are outlined, including synthetic route design, excipient selection and precursor control, crystallization optimization, and storage conditions, all framed within a tiered, life-cycle-based risk management model aligned with ICH Q9(R1), ICH Q10, and the Carcinogenic Potency Categorization Approach (CPCA). The role of in silico tools, including structure–activity relationships (SAR), quantitative SAR (QSAR), and machine learning (ML), is emphasized in supporting nitrosation risk prediction and acceptable intake (AI) estimation.
This article presents a holistic approach to the design, control, and safety analysis of cumene oxidation. This holistic approach involves a systematic, step-by-step study of cumene oxidation, integrating design, economic optimization, and process safety through mathematical modeling. A dynamic model of the cumene oxidation process was developed, featuring an optimized plantwide control structure. The safety analysis was conducted using a 'modified' HAZOP approach, which involves systematic identification of failure scenarios followed by a quantitative sensitivity analysis of their consequences to deviations in controller parameters based on the developed dynamic model. The results indicate that the most significant process disturbances arise, primarily, from failures in the cumene recycle line. Specific recommendations are provided to mitigate disturbances associated with recycle line failures. Furthermore, the study demonstrates that other investigated failures can also lead to substantial deviations of key process variables from steady-state conditions. Notably, a simulated failure of the reactor cascade cooling system revealed significant thermal inertia in response to external disturbances. These simulation results support the potential implementation of cumene oxidation catalysts, provided that emergency scenarios in the cooling system of the reactor cascade are shown to remain manageable under catalytic operating conditions. Applying this integrated approach to catalytic cumene oxidation ensures a comprehensive evaluation of the technical, economic, and safety parameters of the industrial plant.
Chemicals containing peroxy functionality, which are known to be thermally unstable but industrially useful, have been associated with several industrial incidents. In this study, the thermal decomposition characteristics and autocatalytic potential of 40 organic peroxides were systematically investigated using differential scanning calorimetry (DSC) and accelerating rate calorimetry (ARC). Dynamic DSC and ARC results indicated that most samples presented significant thermal hazards, characterized by low onset temperatures (T onset), high decomposition enthalpies (Delta H), large adiabatic temperature rise (Delta T ad), and short time-to-maximum-rate. Distinctive structure-dependent stability was observed, with peroxy dicarbonates generally exhibiting lower T onset values, while dialkyl peroxides showed comparatively higher thermal stability. Isothermal DSC identified 18 organic peroxides exhibiting a typical induction-acceleration-decay heat flow profile, indicative of autocatalytic behavior. Autocatalytic manifestation was more frequently observed in diacyl peroxides, peroxy esters, and peroxy dicarbonates, whereas dialkyl peroxides, alkylperoxyl carbonates, and ketone peroxides showed no detectable autocatalytic features. Concentration-gradient experiments further demonstrated that autocatalytic behavior is formulation-dependent and may attenuate upon dilution. These experimental findings provide systematic evidence for structure-hazard correlations and highlight the necessity of individual evaluation of organic peroxide formulations to ensure reliable thermal risk assessment.
This study examined airborne alpha-diketones (diacetyl and 2,3-pentanedione), known respiratory irritants, across different coffee processing variables. A factorial design assessed the roast degree and processing stage, supported by separate tests on other factors. All experiments were performed in triplicate at a single site to reduce variability. Real-time measurements of total volatile organic compounds (TVOCs) and PM2.5 aerosols were also conducted when possible. Significant differences (p < 0.05) in alpha-diketone concentrations were found among processing stages (grinding > roasting > packaging), but not by roast degree. Grinding emitted much higher TVOC levels than roasting (1131.7 vs 88.3 ppb), while roasting produced higher PM2.5 (1079.6 vs 90.1 mu g/m(3)). TVOC levels strongly correlated with diacetyl (r = 0.827) and 2,3-pentanedione (r = 0.848). Beans from Brazil (sun-dried) released more alpha-diketones than Ethiopian (washed) beans. Grinding zone levels of diacetyl and 2,3-pentanedione were 8.4 and 4.9 times higher, respectively, than in the nearby bar area (similar to 7 m away). Bean aging affected 2,3-pentanedione levels, peaking on the first day after roasting. Grind size and quantity showed no significant effects. These findings highlight key factors influencing alpha-diketone emissions, and reflect realistic exposures in cafe environment. Routine monitoring and source control are essential to protect worker and consumer health. Elevated PM2.5 levels during roasting also merit further study on their chemical characteristics and toxicity.
This case report brings the hazards of spontaneous ignition of black TiO2, produced by thermal reduction of titania with sodium borohydride (NaBH4) at laboratory scale, to attention. Although the reduction method is widely cited in the literature without indicating any hazards and despite following these published protocols, in several syntheses spontaneous ignition of the reduced samples was observed only seconds after the reduced titania samples were exposed to ambient air at room temperature. Although the exact reduction mechanism and reason for ignition are not yet fully understood, the formation of (complex) metal hydrides of sodium and/or titania could be the cause. These frequent incidents stress the need for thorough risk assessment beyond literature procedures, detailed reporting of experimental conditions, and open reporting of laboratory-scale incidents and near misses to better protect researchers working with highly reactive reducing agents. Furthermore, TGA-MS analysis was performed to unravel the reduction mechanism, revealing that the plastic tubing of the analytical instrument allowed an O2/H2O influx into the system. This highlights a potential additional problem with respect to the quality of analysis of samples that can be easily oxidized and also points to potentially hazardous situations when the oxidation is highly exothermic.
Many reagents needed for the synthesis of important compounds are air- and/or moisture-sensitive. Technologies for handling these chemicals have focused on the use of puncturable membranes, which present a number of issues, ranging from safety concerns to increased waste. A new adapter has been designed and tested that allows for the safe transfer and effective protection of air-sensitive reagents.
University laboratory safety, particularly in settings involving chemical substances, is characterized by complexity and systemic, making it difficult for traditional safety management methods to effectively address its risks. This study proposes a hybrid model, establishing a comprehensive methodology of "systemic diagnosis-quantitative assessment-targeted intervention" for the periodic evaluation of laboratory safety. The model first applies the System Theoretic Accident Model and Processes (STAMP) to construct a three-tier control structure of "university, secondary units, laboratories", systemically identifying safety constraints and potential unsafe control actions (UCAs) to qualitatively diagnose the root causes of system vulnerabilities. Subsequently, the results from STAMP provide the scientific basis for constructing the indicator system of the analytic hierarchy process (AHP), while the fuzzy comprehensive evaluation (FCE) is employed to handle ambiguities in the assessment, enabling the quantitative evaluation of laboratory safety conditions. Finally, the effectiveness of the hybrid model was validated using chemistry laboratories in a university. The model not only generated a comprehensive safety score of 89.07 for the laboratories, categorizing it as "generally safe" level, but also precisely identified key weak indicators such as "insufficient safety budget" and "failure to close the loop in hazard rectification." The model profoundly revealed underlying issues and proposed targeted improvement strategies, offering an innovative theoretical framework and practical tool for university laboratory safety management.
In contrast to n-order reactions, autocatalytic reactions exhibit a characteristic where the products of the reaction further catalyze the process, which may significantly increase the risk of thermal runaway under certain conditions. In this work, a dimensionless mathematical model is established to characterize heterogeneous liquid-liquid autocatalytic reactions, with general applicability to any reaction order. Then, an artificial neural network (ANN) criterion is introduced to assess the thermal behavior of autocatalytic reactions, using seven representative features for characterization. To prevent overfitting, Bayesian regularization is applied during training, and particle swarm optimization (PSO) is used to determine the initial weights and thresholds. The results indicate that the established ANN criterion achieves high accuracies on training, validation, and test sets, all of which are more than 99%. The effectiveness of the ANN criterion is validated using a real autocatalytic reaction case. In practice, thermal behavior can be rapidly identified by evaluating reaction features at the current jacket temperature, offering an innovative strategy for identifying safe and efficient operating conditions for autocatalytic reactions.
