Molecular composition limits the reaction kinetics of riverine dissolved organic matter decomposition.

The bioavailability and degradation of riverine dissolved organic matter (DOM) play crucial roles in greenhouse gas emissions; however, studies on the kinetic decomposition of fluvial DOM remain scarce. In this study, the decomposition kinetics of dissolved organic carbon (DOC) were characterized using the reactivity continuum model through 28-day bio-incubation experiments with water samples from the Yangtze River. The relationship between DOM composition and decomposition kinetics was analyzed using optical and molecular characterization combined with apparent decay coefficients. Our results revealed that DOM compounds rich in nitrogen and sulfur were predominantly removed, exhibiting a transition from an unsaturated to a saturated state following microbial degradation. These heteroatomic compounds, which constituted 75.61 % of the DOM compounds positively correlated with the decay coefficient k0, underwent preferential degradation in the early stages of bio-incubation due to their higher bioavailability. Additionally, we observed that S-containing fractions with high molecular weight values (MW > 400 Da) may be associated with larger reactivity grades. This study underscored the complex interplay between DOM composition and its kinetic decomposition in river ecosystems, providing further support for the significance of molecular composition in large river DOM as crucial factors affecting decomposition.

Prediction of the performance of pre-packed purification columns through machine learning.

Pre-packed columns have been increasingly used in process development and biomanufacturing thanks to their ease of use and consistency. Traditionally, packing quality is predicted through rate models, which require extensive calibration efforts through independent experiments to determine relevant mass transfer and kinetic rate constants. Here we propose machine learning as a complementary predictive tool for column performance. A machine learning algorithm, extreme gradient boosting, was applied to a large data set of packing quality (plate height and asymmetry) for pre-packed columns as a function of quantitative parameters (column length, column diameter, and particle size) and qualitative attributes (backbone and functional mode). The machine learning model offered excellent predictive capabilities for the plate height and the asymmetry (90 and 93%, respectively), with packing quality strongly influenced by backbone (∼70% relative importance) and functional mode (∼15% relative importance), well above all other quantitative column parameters. The results highlight the ability of machine learning to provide reliable predictions of column performance from simple, generic parameters, including strategic qualitative parameters such as backbone and functionality, usually excluded from quantitative considerations. Our results will guide further efforts in column optimization, for example, by focusing on improvements of backbone and functional mode to obtain optimized packings.

Ammonium (NH4+) transport processes in the riverbank under varying hydrologic conditions.

Attenuation of groundwater ammonium (NH4+) is expected to occur through redox reaction and adsorption of the riverbank. Previous studies determined that NH4+ mostly degraded through nitrification along subsurface flow, however, the adsorption capacities of riverbanks were always ignored in the NH4+ reduction processes. In this study, field experiments were conducted in the Fuliji section of the Xiaosuixin River, China, to understand NH4+ transport and attenuation under rainfall events-induced river and groundwater interactions. The results indicated that the NH4+ concentration in river water increased significantly after heavy rainfall events and reached a peak of about 5.88 mg L-1, and the lag time was more than 2 weeks compared with the river peak stage. Adsorption plays a dominant role in attenuation of NH4+ in riverbank with high amounts of organic materials and clay minerals, reducing its concentration to less than 0.05 mg L-1. A two-dimensional lateral exchange and transport model of NH4+ was developed and calibrated against observations in the aquifer, and an exponential reduction pattern of NH4+ was identified. The model's possible implications about the effects of varying hydrologic changes (i.e., peak stage and lag time differences between river and groundwater) on NH4+ transport were also discussed. Thus, the effects of river-groundwater interactions on nitrogen pollution should be taken into consideration in river regulation strategies in order to ensure proper hydrogeochemical functioning of river-aquifer interfaces and related ecosystems.

Flexible material formulations for 3D printing of ordered porous beds with applications in bioprocess engineering.

BACKGROUND: 3D printing is revolutioning many industrial sectors and has the potential to enhance also the biotechnology and bioprocessing fields. Here, we propose a new flexible material formulation to 3D print support matrices with complex, perfectly ordered morphology and with tuneable properties to suit a range of applications in bioprocess engineering. FINDINGS: Supports were fabricated using functional monomers as the key ingredients, enabling matrices with bespoke chemistry, such as charged groups, chemical moieties for further functionalization, and hydrophobic/hydrophilic groups. Other ingredients, e.g. crosslinkers and porogens, can be employed to fabricate supports with diverse characteristics of their porous network, providing an opportunity to further regulate the mechanical and mass transfer properties of the supports. Through this approach, we fabricated and demonstrated the operation of Schoen gyroid columns with (I) positive and negative charges for ion exchange chromatography, (II) enzyme bioreactors with immobilized trypsin to catalyse hydrolysis, and (III) bacterial biofilm bioreactors for fuel desulphurization. CONCLUSIONS: This study demonstrates a simple, cost-effective, and flexible fabrication of customized 3D printed supports for different biotechnology and bioengineering applications.

Catalytic effect and mechanism of in-situ metals on pyrolysis of FR4 printed circuit boards: Insights from kinetics and products.

Pyrolysis is a promising method for the recovery of waste printed circuit boards (WPCBs), but few researches have noticed the influence of in-situ metals. This study conducted a series of comparisons between metal-free leftover pieces (LP) and intact boards (IB), including pyrolysis characteristics, volatile emission, kinetics, and thermodynamic parameters. The thermo-gravimetry (TG) analyses indicated that both the samples presented predominant mass loss in narrow temperature intervals, and characteristic pyrolysis temperatures of IB were approximately 15 °C lower than those of LP. Dominant constituents in evolved gases were detected by Fourier-transform infrared spectrometry as CO2, phenol, bromophenol, ethers, ketones, and aldehydes, and metals accelerated the generation of light hydrocarbons and aromatic compounds. The activation energy and thermodynamic parameters were calculated and compared, and the results verified the presence of in-situ metals led to a lower energy barrier and higher reaction extent. Moreover, conversion behaviors of Cu, Fe, Sn, and Pb manifested the formation of metal bromides and implied the reduction of brominated volatiles. The obtained results confirmed the catalytic effect of in-situ metals on PCBs pyrolysis and their bromine fixation abilities. This study contributes to fundamental knowledge that can be used to guide the pyrolysis of WPCBs.