Production Rate Research Articles

An E115A Missense Variant in CERS2 Is Associated With Increased Sleeping Energy Expenditure and Hepatic Insulin Resistance in American Indians.

Genetic determinants of inter-individual differences in energy expenditure (EE) are largely unknown. Sphingolipids, such as ceramides, have been implicated in the regulation of human EE via mitochondrial uncoupling. In this study, we investigated whether genetic variants within enzymes involved in sphingolipid synthesis and degradation affect EE and insulin-related traits in a cohort of American Indians informative for 24-hour EE and glucose disposal rates during a hyperinsulinemic-euglycemic clamp. Association analysis of 10,084 genetic variants within 28 genes involved in sphingolipid pathways identified a missense variant (rs267738, A>C, E115A) in exon 4 of CERS2 that was associated with higher sleeping EE (+116 kcal/day) and increased rates of endogenous glucose production during basal (+5%) and insulin-stimulated (+43%) conditions, both indicators of hepatic insulin resistance. The rs267738 variant did not affect ceramide synthesis in HepG2 cells but resulted in a 30% decrease in basal mitochondrial respiration. In conclusion, we provide evidence that the CERS2 rs267738 missense variant may influence hepatic glucose production and post-absorptive sleeping metabolic rate.

Decorating ZnIn2S4 with earth-abundant Co9S8 and Ni2P dual cocatalysts for boosting photocatalytic hydrogen evolution

Due to the synergistic effect of the rapid transfer of photo-generated electrons and enrichment of reactive active sites, reasonably assembling dual cocatalysts with semiconductor is considered as an excellent method to enhance the photocatalytic properties of semiconductors. Herein, earth-abundant Co9S8 and Ni2P dual cocatalysts modified-ZnIn2S4 (ZIS/CS/NP) composite photocatalyst has been constructed by the low-temperature solvothermal and in-situ photodeposition methods. A series of characterizations indicate the hollow structure of Co9S8 can reduce the charge transfer distance and enhance the migration of photo-generated electrons, while Ni2P acts as an electron collector and provides sufficient active sites for H2 release in this dual cocatalysts system. Because of the synergistic effect of dual cocatalysts, ZIS/CS/NP composites exhibit superior activity in the photocatalytic H2 production compared to blank ZIS, ZIS/CS and ZIS/NP. Notably, the optimal photocatalytic H2 production rate of ZIS/CS/NP reaches 6823.64 μmol g−1 h−1, which is approximately 86 times higher that of blank ZIS. This work is expected to provide useful reference for the construction of high-performance dual-cocatalyst modified semiconductor composite for photocatalytic H2 evolution.

Effects of temperature, chloride and perchlorate salt concentration on the metabolic activity of Deinococcus radiodurans

The extremophile bacterium Deinococcus radiodurans is characterized by its ability to survive and sustain its activity at high levels of radiation and is considered an organism that might survive in extraterrestrial environments. In the present work, we studied the combined effects of temperature and chlorine-containing salts, with focus on perchlorate salts which have been detected at high concentrations in Martian regolith, on D. radiodurans activity (CO2 production rates) and viability after incubation in liquid cultures for up to 30 days. Reduced CO2 production capacity and viability was observed at high perchlorate concentrations (up to 10% w/v) during incubation at 0 or 25 °C. Both the metabolic activity and viability were reduced as the perchlorate and chloride salt concentration increased and temperature decreased, and an interactive effect of temperature and salt concentration on the metabolic activity was found. These results indicate the ability of D. radiodurans to remain metabolically active and survive in low temperature environments rich in perchlorate.

Regulation of morphology and charge transfer mechanism: Oxygen vacancy-rich Mn0.2Cd0.8S/CoMoO4 S-type heterojunction promoting photocatalytic hydrogen production through hydrothermal in-situ synthesis

The development of efficient photocatalysts is a very interesting topic for sustainable hydrogen production using solar energy. In this paper, Mn0.2Cd0.8S was loaded on CoMoO4 nanorods by in-situ solvothermal method. Through morphological control, two rod-shaped elemental materials were loaded in situ to construct a composite with a ball shaped bouquet structure. The Mn0.2Cd0.8S/CoMoO4 photocatalyst with enriched oxygen vacancies was synthesized by high-temperature annealing. It is worth noting that by constructing an heterojunction, the Mn0.2Cd0.8S/CoMoO4 photocatalyst exhibits high catalytic activity and stability. The optimal H2 production rate of Mn0.2Cd0.8S/CoMoO4 was 7.04mmol·g−1·h−1. Surprisingly, the hydrogen production rate of the composite was 35.88 times that of CoMoO4. Density functional theory (DFT) calculations showed that Mn0.2Cd0.8S/CoMoO4 has electron attraction ability and strong conductivity. X-ray photoelectron spectroscopy (XPS) confirmed the charge transfer path of the heterojunction between Mn0.2Cd0.8S and CoMoO4, which greatly promoted the spatial charge separation. This work provides a reference for combining heterojunction design with defect engineering to achieve efficient conversion of solar energy to chemical energy.

Novel THAI well arrangements for improving heavy oil recovery rate in comparison to that achievable in the conventional THAI in situ combustion technology

The toe-to-heel air injection (THAI) is an environmentally friendly process for in situ upgrading of heavy oils and bitumen via in situ combustion (ISC). Unlike the conventional ISC that uses a vertical producer, the THAI process uses a horizontal producer (HP) well to produce the upgraded oil to the surface. Recent field data has shown that the THAI process is a relatively low-oil-production-rate technology. These considerations call for innovative solutions so that the oil production rate is improved, whilst propagating a stable, efficient, and safe combustion front. Consequently, this work has provided those solutions. Through numerical reservoir simulations, using CMG STARS, five completely new THAI well configurations, which have been labeled A01, A02, A03, A04, and A05, have been developed and studied, and their performances are compared against that of the conventional THAI process which has wells arranged in a classic SLD pattern (i.e. the best-performing conventional THAI process) and against each other. The THAI arrangements A02-A04, however, assume a horizontal air injection well, which currently is not used in field practice but may be developed in the future. In field practice, THAI is applied in a line drive configuration starting up-dip and going down on the structure whilst taking advantage of the contribution provided by the drainage due to gravity; the expansion is made one way only (by drilling new patterns in one direction only). For this reason, the classic SLD pattern refers to the case where the dip of the reservoir is significant (>2-3 degrees). However, when the dip of the reservoir is not significant (“flat” reservoirs) there is a possibility to expand the process in both opposing directions. This is the case dealt with in this work. All configurations A01-A05 assume expansion of the THAI commercial operation in both directions. Selection criteria have been developed and used to determine the two best performing processes. For example, in terms of long-term stability, safety, and efficiency of the combustion process, a process named model A01 is the best, as it achieved 99.8% oxygen utilization when compared with any other model. It also achieves oil recovery, due to two years of combustion only, of 30.78% OOIP, which is greater than that in the base case model. Overall, based on the weighted selection criteria, which are developed from the deepest analyses of the quantitative 1-dimensional time-dependent parameters and from thorough analyses of the qualitative 2-dimensional profiles of temperature, the combustion zone in the form of oxygen mole fraction, and the oil-flow dynamics inside the reservoir in the form of oil saturation, then model A01 is the best and is followed by model A03. The overall performance of each of these two novel methods outweighs that of the conventional THAI process. However, model A03 uses a horizontal well for injection, which is not current field practice. Therefore, future developmental work should concentrate on the novel method A01 for upgrading and recovery of heavy oils and bitumen, especially since this is low-carbon, efficient, wastewater-free, and provides upgrading inside the reservoir and hence it has a low surface footprint.

Machine learning-based predictive control of an electrically-heated steam methane reforming process

Hydrogen plays a crucial role in improving sustainability and offering a clean and efficient energy carrier that significantly reduces greenhouse gas emissions. However, the primary method of industrial hydrogen production, steam methane reforming (SMR), relies on the combustion of hydrocarbons as the heating source for the reforming reactions, resulting in significant carbon emissions. To address this issue, an experimental setup of an electrically-heated steam methane reformer (e-SMR) has been constructed at UCLA, and a lumped first-principle dynamic process model was built based on parameters estimated from the experimental data in a previous study. Subsequently, the first-principle dynamic process model was implemented into the computational model predictive control (MPC) scheme, successfully driving the hydrogen production rate to the desired setpoint. While these works are important and pave the way for developing MPC for large-scale e-SMR processes, the first-principle process model may not accurately reflect the actual process behavior, particularly as the process behavior changes with time. Therefore, the development and establishment of an adaptive data-driven approach for implementing model predictive control in the e-SMR process is necessary. To address this need, the present work investigates the construction of recurrent neural network (RNN) models for an e-SMR process in-depth, utilizing data from an experimentally-validated first-principle model. Specifically, a long short-term memory (LSTM) layer was utilized in the RNN model to effectively capture the complex correlations present in long-term sequential data. Subsequently, this LSTM-based RNN process model was employed to design an MPC, and its performance was evaluated through comparison with proportional-integral (PI) control. To address potential disturbances and variability in a typical e-SMR process, three distinct approaches were developed: MPC with an integrator, MPC with real-time online retraining (transfer learning), and offset-free MPC. These approaches effectively eliminated the offset caused by disturbances. Overall, this study underscores the effectiveness of utilizing RNN models to capture process dynamics in an experimental e-SMR process. It also outlines strategies for employing RNN-based control and multiple approaches to address disturbances in general processes with partially infrequent and delayed measurement feedback. This approach is particularly valuable in scenarios where developing first-principle models for a new process may be challenging.

Enhancing economic sustainability in mature oil fields: Insights from the clustering approach to select candidate wells for extended shut-in

Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts. In periods of significant price drops, companies may consider extended duration of well shut-ins (i.e. temporarily stopping oil production) for economic reasons. For example, prices during the early days of the Covid-19 pandemic forced operators to consider shutting in all or some of their active wells. In the case of partial shut-in, selection of candidate wells may evolve as a challenging decision problem considering the uncertainties involved. In this study, a mature oil field with a long (50+ years) production history with 170+ wells is considered. Reservoirs with similar conditions face many challenges related to economic sustainability such as frequent maintenance requirements and low production rates. We aimed to solve this decision-making problem through unsupervised machine learning. Average reservoir characteristics at well locations, well production performance statistics and well locations are used as potential features that could characterize similarities and differences among wells. While reservoir characteristics are measured at well locations for the purpose of describing the subsurface reservoir, well performance consists of volumetric rates and pressures, which are frequently measured during oil production. After a multivariate data analysis that explored correlations among parameters, clustering algorithms were used to identify groups of wells that are similar with respect to aforementioned features. Using the field’s reservoir simulation model, scenarios of shutting in different groups of wells were simulated. Forecasted reservoir performance for three years was used for economic evaluation that assumed an oil price drop to $30/bbl for 6, 12 or 18 months. Results of economic analysis were analyzed to identify which group(s) of wells should have been shut-in by also considering the sensitivity to different price levels. It was observed that wells can be characterized in the 3-cluster case as low, medium and high performance wells. Analyzing the forecasting scenarios showed that shutting in all or high- and medium-performance wells altogether results in better economic outcomes. The results were most sensitive to the number of active wells and the oil price during the high-price period. This study demonstrated the effectiveness of unsupervised machine learning in well classification for operational decision making purposes. Operating companies may use this approach for improved decision making to select wells for extended shut-in during low oil-price periods. This approach would lead to cost savings especially in mature fields with low-profit margins.

Constructing GO films/CdS nanoparticles/MoS2 nanosheets ternary nanojunction with enhanced photocatalytic hydrogen evolution activity

CdS, as a visible-light responsible photocatalyst, has gained considerable attentions for its relatively narrow bandgap and negative conduction band edge. However, its photocatalytic activity is hindered by challenges such as severe charge recombination, insufficient active sites and poor stability. In this work, a robust strategy is proposed to optimize the transfer channel for charge carriers and enhance the active sites by heterojunction and cocatalyst engineering. The well-designed GO/CdS/MoS2 shows remarkable durability (20h without significant loss of H2 production rate) with a high photocatalytic H2 production rate of 1.45mmolh-1g-1 (about 6.6-fold enhancement compared with CdS) at GO content of 0.1wt % and MoS2 loading amount of 5.0wt %. The bolstered H2 production rate is attributed to the augmented transfer channels of mass owing to the holey structure of GO, the enhanced active sites since the introduction of MoS2, and the unidirectional transfer of electrons from CdS to MoS2, which is ascribed to the built-in electric field in the CdS/MoS2 interface. Thanks to the directional transfer of holes from CdS to GO in the GO/CdS interface, the assemblage of holes in CdS is prevented, thus contributing to the amplified stability GO/CdS/MoS2. Our findings offer insights for the enhanced photocatalytic activity of CdS based photocatalysts, which may inspire the development of the heterostructure photocatalysts for solar-to-fuel conversion.

High production of enantiopure (R,R)-2,3-butanediol from crude glycerol by Klebsiella pneumoniae with an engineered oxidative pathway and a two-stage agitation strategy

Background(R,R)-2,3-butanediol (BDO) is employed in a variety of applications and is gaining prominence due to its unique physicochemical features. The use of glycerol as a carbon source for 2,3-BDO production in Klebsiella pneumoniae has been limited, since 1,3-propanediol (PDO) is generated during glycerol fermentation.ResultsIn this study, the inactivation of the budC gene in K. pneumoniae increased the production rate of (R,R)-2,3-BDO from 21.92 ± 2.10 to 92.05 ± 1.20%. The major isomer form of K. pneumoniae (meso-2,3-BDO) was shifted to (R,R)-2,3-BDO. The purity of (R,R)-2,3-BDO was examined by agitation speed, and 98.54% of (R,R)-2,3-BDO was obtained at 500 rpm. However, as the cultivation period got longer, the purity of (R,R)-2,3-BDO declined. For this problem, a two-step agitation speed control strategy (adjusted from 500 to 400 rpm after 24 h) and over-expression of the dhaD gene involved in (R,R)-2,3-BDO biosynthesis were used. Nevertheless, the purity of (R,R)-2,3-BDO still gradually decreased over time. Finally, when pure glycerol was replaced with crude glycerol, the titer of 89.47 g/L of (R,R)-2,3-BDO (1.69 g/L of meso-2,3-BDO), productivity of 1.24 g/L/h, and yield of 0.35 g/g consumed crude glycerol was achieved while maintaining a purity of 98% or higher.ConclusionsThis study is meaningful in that it demonstrated the highest production and productivity among studies in that produced (R,R)-2,3-BDO with a high purity in Klebsiella sp. strains. In addition, to the best of our knowledge, this is the first study to produce (R,R)-2,3-BDO using glycerol as the sole carbon source.

Co-doping regulation on Ni-based electrocatalysts to adjust the selectivity of oxygen reduction reaction for Zn-air batteries and H2O2 production.

Although Ni-based materials are widely used as electrocatalysts, it remains necessary to further explore their selectivity towards the four- or two-electron oxygen reduction reaction (ORR). Herein, it is proposed to synthesize NiO@NCNTs (NCNTs = N-doped carbon nanotubes) using a metal-organic framework (MOF), [Ni(BZIDA)(H2O)]n (NiMOF, BZIDA = benzimidazole-5,6-dicarboxylic acid), as a precursor after calcination with dicyandiamide (DCDA). Regarding NiO@NCNTs, small NiO particles are distributed in NCNTs derived from DCDA homogeneously. NiO@NCNTs act as a typical two-electron electrocatalyst. The H2O2 production rate of NiO@NCNTs reaches 0.5 mol g-1 h-1 at 0.46 V (vs. RHE). After the doping of Co2+ in NiMOF, Co/NiO@NCNTs were synthesized using a similar method, with the four-electron character shown in ORR. A Zn-air battery was assembled by applying Co/NiO@NCNTs as the cathode material. When discharge occurs at 5 and 10 mA cm-2, its specific capacitance reaches 779.3 and 832.2 mA h g-1 with an energy density of 928.6 and 948.5 W h kg-1, respectively. Theoretical calculations suggest a variation in ORR selectivity between NiO@NCNTs and Co/NiO@NCNTs, which results from their different interactions with OOH*. This study demonstrates the effect of the structure on ORR selectivity for Ni-based electrocatalysts.

Integration of seismic modeling and interpretation of real seismic data for the detection and characterization of carbonate stringers: example from Oman

Carbonate stringers are well known geological targets in Oman due to their high estimated prospective resources and production rate, especially in the South Oman Salt Basin (SOSB). A carbonate stringer is defined as a slab of rock completely encased inside the salt body. In this study, we integrate seismic attribute analysis, structural interpretation, seismic modeling, inversion and analog fields to understand and delineate carbonate stringers from South Oman Salt Basin. We use a newly processed seismic data acquired over the area on which we carry out a thorough seismic attribute analysis to help us build a seismic model for the salt structures of the area. A major challenge we faced in the study is the lack of well data. We thus generate pseudo wells at some selected areas based on results from our attribute analysis, structural interpretation, and initial inversion results. The pseudo wells helped improve the seismic inversion and produce more accurate impedance models. The results from seismic inversion and from seismic attribute analysis inferred that some stringers that were difficult to see in seismic data are visible in impedance and some attributes. The conducted study not only increased the confidence related to identifying and characterizing carbonate stringers in the area, but also allowed us to propose some potential stringers for future drilling programs.

Cathode cooling effects on the neutron production rate in the glow discharge type of fusion neutron source

Fusion reactions on the cathode surface of glow discharge deuterium–deuterium fusion neutron sources contribute significantly to the neutron production rate (NPR). While the NPR shows a linear relationship with current in the low current regime, a rise in cathode temperature in the high-current regime causes stagnation of the NPR. This tendency may be caused by high-temperature-induced desorption of deuterium on the cathode. This study aims to clarify the relationship between NPR and deuterium desorption. The present study utilized a water-cooling system to prevent deuterium desorption on the cathode. A stainless-steel 304 cathode and a diamond-like carbon (DLC)-coated cathode were tested. The cooling system kept the cathode temperature below 315 K throughout the experiment. In the case of the DLC-coated cathode, the water-cooling system improves the NPR in a high-current regime (30 mA or more in the present study). At 50 kV and 60 mA, the NPRs were 1.87 × 106 and 8.39 × 105 (n/s), with and without water cooling, respectively. Furthermore, without the cooling system, the NPR correlation with the cathode temperature indicates good agreement with the estimation model of deuterium desorption on the DLC-coated cathode. This study demonstrates that suppression of deuterium desorption in the cathode improves NPR, especially in the high-current regime.

In-situ biological biogas upgrading using upflow anaerobic polyfoam bioreactor: Operational and biological aspects.

A high rate upflow anaerobic polyfoam-based bioreactor (UAPB) was developed for lab-scale in-situ biogas upgrading by H2 injection. The reactor, with a volume of 440 mL, was fed with synthetic wastewater at an organic loading rate (OLR) of 3.5 g COD/L·day and a hydraulic retention time (HRT) of 7.33 h. The use of a porous diffuser, alongside high gas recirculation, led to a higher H2 liquid mass transfer, and subsequently to a better uptake for high CH4 content of 56% (starting from 26%). Our attempts to optimize both operational parameters (H2 flow rate and gas recirculation ratio, which is the total flow rate of recirculated gas over the total outlet of gas flow rate) were not initially successful, however, at a very high recirculation ratio (32) and flow rate (54 mL/h), a significant improvement of the hydrogen consumption was achieved. These operational conditions have in turn driven the methanogenic community towardthe dominance of Methanosaetaceae, which out-competed Methanosarcinaceae. Nevertheless, highly stable methane production rates of 1.4-1.9 L CH4/Lreactor.day were observed despite the methanogenic turnover. During the different applied operational conditions, the bacterial community was especially impacted, resulting in substantial shifts of taxonomic groups. Notably, Aeromonadaceae was the only bacterial group positively correlated with increasing hydrogen consumption rates. The capacity of Aeromonadaceae to extracellularly donate electrons suggests that direct interspecies electron transfer (DIET) enhanced biogas upgrading. Overall, the proposed innovative biological in-situ biogas upgrading technology using the UAPB configuration shows promising results for stable, simple, and effective biological biogas upgrading.

Source separation and anaerobic co-digestion of blackwater and food waste for biogas production and nutrient recovery

ABSTRACT Anaerobic co-digestion of source-separated blackwater (BW) and food and kitchen waste (FW) offers decentralized circular economy solutions by enabling local production of biogas and nutrient-rich byproducts. In this study, a 2 m3 pilot-scale continuously stirred tank reactor (CSTR) operated under mesophilic conditions was utilized for co-digestion of BW and FW. The process obtained a CH4 yield of 0.7 ± 0.2 m3/kg influent-volatile solid (VS), reaching a maximum yield of 1.1 ± 0.1 m3/kg influent-VS, with an average organic loading rate of 0.6 ± 0.1 kg-VS/m3/d and HRT of 25 days. The CH4 production rate averaged 0.4 ± 0.1 m3/m3/d, peaking at 0.6 ± 0.1 m3/m3/d. Treatment of digestate through flocculation followed by sedimentation recovered over 90% of ammonium nitrogen and potassium, and 80–85% of total phosphorus in the liquid fraction. This nutrient-rich liquid was used to cultivate Chlorella vulgaris, achieving a biomass concentration of 1.2 ± 0.1 g/L and 85 ± 3% and 78 ± 5% ammonium nitrogen and phosphorus removal efficiency, respectively. These findings not only highlight the feasibility of anaerobic co-digestion of source-separated BW and FW in local biogas production but also demonstrate the potential of microalgae cultivation as a sustainable approach to converting digestate into nutrient-rich algae biomass.

Substituent Modulated Electronic Properties of Cu(I) Active Site in Metal–Organic Halides for Boosting Hydrogen Evolution Reaction†

Comprehensive SummaryDevelopment of heterogeneous molecular photocatalysts for promising light‐driven hydrogen evolution reaction (HER) is highly demanding but still challenging. Here, we report the blue‐greenish emitting dinuclear metal–organic halides as photocatalyst by incorporating site‐specific single copper(I) atoms that exhibit an efficient carbon‐negative H2 production. Interestingly, the electronic properties, including the spin and charge density of central Cu(I) active site, can be triggered by substituent modulation in metal–organic halides, which greatly affect the exciton dissociation kinetics and thus the HER reactivity. The optimized spin density in these heterogeneous photocatalysts drastically boosts the hydrogen production rate from 1250 to 3130 μmol·g–1·h–1. Our molecular strategy provides a platform that rationally facilitates electronic modulation of copper(I) atoms, tunes the macroscopic optoelectronic properties of photocatalysts and boosts carbon‐negative HER activity, extending the boundaries of conventional molecular‐based photocatalysts.

Production of hidden-heavy and double-heavy hadronic molecules at the Z factory of CEPC

With a clean environment and high collision energy, the Circular Electron Positron Collider (CEPC) would be an excellent facility for heavy flavor physics. Using the Monte Carlo event generator ythia, we simulate the production of the charmed (bottom) hadron pairs in the electron-positron collisions at the Z factory of CEPC, and the inclusive production rates for typical candidates of the hidden/double-charm and hidden/double-bottom S-wave hadronic molecules are estimated at an order-of-magnitude level with the final state interactions after the hadron pair production. The predicted cross sections for the hidden-charm meson-meson molecules X(3872) and Zc(3900) are at pb level, which are about two to three orders of magnitude larger than the production cross sections for the double-charm meson-meson molecules Tcc and Tcc*, as the double-charmed ones require the production of two pairs of cc¯ from the Z boson decay. The production cross sections for the hidden-charm pentaquark states Pc and Pcs as meson-baryon molecules are a few to tens of fb, which are about one magnitude larger than those of the possible hidden-charm baryon-antibaryon and double-charm meson-baryon molecules. In the bottom sector, the production cross sections for the Zb states as B(*)B¯* molecules are about tens to hundreds of fb, indicating 106–107 events from a two-year operation of CEPC, and the expected events from the double-bottom molecules are about 2–5 orders of magnitude smaller than the Zb states. Our results shows great prospects of probing heavy exotic hadrons at CEPC. Published by the American Physical Society 2024

Industrial Catalytic Production Process of Erythromycin

The impact of COVID-19’s unexpected outbreak forced the scientific community to seek alternative treatment methods in order to overcome the hindrance of traditional medicine in terms of alleviating the symptoms of this virus. Erythromycin, which was introduced in 1952, is an antibiotic that is reported to pose as an effective substitute medication for various ailments such as skin, respiratory, bone, and female reproductive conditions, and cancer, as well as the newly added COVID-19. The importance of both the erythromycin molecule and the catalyst of its production, namely P450eryF of the cytochrome P450 family, in many health-concerned and environmentally related applications, has led several countries, the World Health Organization (WHO) and the health industry to recruit and cooperate with numerous universities and institutions, in an attempt to tackle the demand for efficient antibiotics. The aim of this study is to discuss and further analyze the overall structure and catalytic mechanism of erythromycin’s synthesis and industrial production, in order to gain a better comprehension of this molecule’s significance and value in the pharmaceutical field. This was carried out through the citation of the current production rates per country and the latest statistics and published patents. As implied in this manuscript, the demand for an increase and improvement in the production of erythromycin and its antibiotic derivatives should be globally promoted to deliver more effective results against infectious diseases, such as COVID-19.

Cell factory design with advanced metabolic modelling empowered by artificial intelligence

Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.

Eutrophication driven macrophyte-derived organic matter decomposition to methane emission relates to co-metabolism effect in freshwater sediments

Lakes and wetlands play pivotal roles in global organic matter storage, receiving significant inputs of organic material. However, the co-metabolic processes governing the decomposition of these organic materials and their impact on greenhouse gas emissions remain inadequately understood. This study aims to assess the effects of mixed decomposition involving macrophytes and cyanobacteria on carbon emissions. A series of microcosms was established to investigate the decomposition of macrophyte residues and algae over a period of 216 days. A two-component kinetic model was utilized to estimate methane (CH4) production rates. Gas isotope technology was employed to discern the contributions of CH4 produced by macrophyte residues or algae. Quantitative PCR and analysis of 16S rRNA gene amplicons were employed to assess changes in functional genes and microbial communities. There were significant differences in the cumulative carbon release from the decomposition of different plant types due to the addition of carbon sources. After adding algae, the cumulative emission of CH4 increased significantly. The δ13C–CH4 partitioning indicated that CH4 originated exclusively from the fresh organic carbon of macrophyte residues, while it shifted to algae source after adding algae. The synergistic effect of the mixed decomposition on the CH4 emissions was greater than the sum of the individual decompositions. The microbial community richness was higher in the single plant residue treatment compared to the mixed treatment with algae addition, while microbial evenness in the sediment increased steadily in each treatment. Our findings emphasize the pronounced co-metabolic effect observed during the mixed decomposition of macrophytes and cyanobacteria.

Facile fabrication of S-scheme MnCo2S4/ZnIn2S4 heterojunction for photocatalytic H2 evolution

In this work, MnCo2S4 nano-particle and ZnIn2S4 nano-flower were synthesized via a facile hydrothermal method, respectively. The MnCo2S4/ZnIn2S4 heterojunction was subsequently fabricated using a physical solvent evaporation method. The photocatalytic H2 production rate over 10 wt. % MnCo2S4/ZnIn2S4 can reach 17.5 mmol·g−1·h−1 under the irradiation of a 300 W Xe lamp, with 20 % triethanolamine (TEOA) used as a sacrificial agent, which is 3.4 times and 54.6 times that of pure ZnIn2S4 (5.2 mmol·g−1·h−1) and MnCo2S4 (0.3 mmol·g−1·h−1). The improved performance can be attributed to the establishment of an S-scheme charge transfer pathway between MnCo2S4 and ZnIn2S4, which not only enhances charge separation but also preserves active electrons and holes. The formed heterojunction facilitates the broadening of the light absorption spectrum, enhance carriers generation, reduce charge transfer impedance, increase electrochemical activity surface area, and lower H2 evolution overpotential compared to individual components. The work offers a promising way for the development of stable and efficient sulfide heterojunction achieve efficent photocatalytic H2 evolution.