Impact of nanoparticle-based fuel additives on biodiesel combustion: An analysis of fuel properties, engine performance, emissions, and combustion characteristics

Nanoparticles (NPs) are becoming increasingly crucial in academic as well as industrial applications. Nanoparticles’ addition to biodiesel shortens the time it takes for the fuel to ignite, allowing combustion to begin earlier and reducing the amount of heat released and the pressure in the cylinders under full load. Recent review studies have focused on nanoparticle additives used in biodiesel and diesel engines, performance, combustion behavior, and emission properties of biodiesel-powered diesel engines, and stability and combustion characteristics of metal nanoparticles (NPs) and their additive impact on compression ignition engines powered by biodiesel and diesel. However, nanoparticle effects on either biodiesel properties, engine performance, emissions, or combustion have not been comprehensively investigated in these studies. This paper addresses this gap by focusing on cost-effective and sustainable strategies for the development of fuel additives for biodiesel combustion. The literature has demonstrated that the incorporation of NP mixes (CeO2 + Al2O3) with biodiesel fuel improved the overall performance, emission characteristics, and combustion efficiency of the engine. For instance, the addition of TiO2 nanoparticles reduced smoke emission by 32.98 %, carbon monoxide (CO) by 30 % and unburned hydrocarbons (HC) by 28.68 %. Emissions of nitrogen oxide (NOx), CO, HC, and smoke were reduced by 30 %, 60 %, 44 %, and 38 %, respectively, while brake power (Bp) and brake thermal efficiency (BTE) went up by 12 %. This study will show advances and potential areas for nanoparticle-enhanced biodiesel engine improvement, leading to cost-effective and sustainable renewable energy solutions

Effect of surfactants on the convective heat transfer and pressure drop characteristics of ZnO/DIW nanofluids: An experimental study

The advancement of nanotechnology has demonstrated the ability of metal-oxide-based nanofluids (NFs) to produce high heat flux in microscale thermal applications. Convective heat transfer (HTC) and flow characteristics (pressure drop (ΔP) and friction factor (f)) of aqueous ZnO NFs' within a circular mini tube (Di = 1.0 mm, L = 330 mm) were analyzed. Experiments were carried out under steady-state and varying flow rates using 0.012-0.048 wt % of NFs and sodium hexametaphosphate (SHMP) and acetylacetone (ACAC) as surfactants (SFs). Laminar flow and constant wall heat flux conditions were used to assess NFs heat transfer properties, ΔP and f. The viscosity (VC) and thermal conductivity (TC) of NFs exhibited a strong dependence on the operating temperature and NFs concentration. VC and TC increased by increasing the NFs concentration and decreased by increasing the operating temperature. Maximum VC and TC enhancement of 16.75% and 23.70% were achieved for SHMP-stabilised NFs, respectively. The average HTC increased by increasing NFs loading and flow rate, with HTCmax of 17.0% noticed for ACAC-stabilised NFs. The ΔPmax and fmax were 16.0% and 12.0%, respectively. Experimental and theoretical results showed a maximum deviation of ±7.0% and ±4.0%, respectively

Experiences with array-based sequence capture; toward clinical applications

Although sequencing of a human genome gradually becomes an option, zooming in on the region of interest remains attractive and cost saving. We performed array-based sequence capture using 385K Roche NimbleGen, Inc. arrays to zoom in on the protein-coding and immediate intron-flanking sequences of 112 genes, potentially involved in mental retardation and congenital malformation. Captured material was sequenced using Illumina technology. A data analysis pipeline was built that detects sequence variants, positions them in relation to the gene, checks for presence in databases (eg, db single-nucleotide polymorphism (SNP)) and predicts the potential consequences at the level of RNA splicing and protein translation. In the samples analyzed, all known variants were reliably detected, including pathogenic variants from control cases and SNPs derived from array experiments. Although overall coverage varied considerably, it was reproducible per region and facilitated the detection of large deletions and duplications (copy number variations), including a partial deletion in the B3GALTL gene from a patient sample. For ultimate diagnostic application, overall results need to be improved. Future arrays should contain probes from both DNA strands, and to obtain a more even coverage, one could add fewer probes from densely and more probes from sparsely covered regions

Effect of tube diameter and capillary number on platelet margination and near-wall dynamics

The effect of tube diameter $D$ and capillary number $Ca$ on platelet margination in blood flow at $\approx 37\%$ tube haematocrit is investigated. The system is modelled as three-dimensional suspension of deformable red blood cells and nearly rigid platelets using a combination of the lattice-Boltzmann, immersed boundary and finite element methods. Results show that margination is facilitated by a non-diffusive radial platelet transport. This effect is important near the edge of the cell-free layer, but it is only observed for $Ca > 0.2$, when red blood cells are tank-treading rather than tumbling. It is also shown that platelet trapping in the cell-free layer is reversible for $Ca \leq 0.2$. Only for the smallest investigated tube ($D = 10 \mu\text{m}$) margination is essentially independent of $Ca$. Once platelets have reached the cell-free layer, they tend to slide rather than tumble. The tumbling rate is essentially independent of $Ca$ but increases with $D$. Tumbling is suppressed by the strong confinement due to the relatively small cell-free layer thickness at $\approx 37\%$ tube haematocrit.Comment: 16 pages, 10 figure

International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery

Artificial intelligence (AI) is transforming the landscape of medicine, including surgical science and practice. The evolution of AI from rule-based systems to advanced machine learning and deep learning algorithms has opened new avenues for its application in metabolic and bariatric surgery (MBS). AI has the potential to enhance various aspects of MBS, including education and training, decision-making, procedure planning, cost and time efficiency, optimization of surgical techniques, outcome and complication prediction, patient education, and access to care. However, concerns persist regarding the reliability of AI-generated decisions and associated ethical considerations. This study aims to establish a consensus on the role of AI in MBS using a modified Delphi method. A panel of 68 leading metabolic and bariatric surgeons from 35 countries participated in this consensus-building process, providing expert insights into the integration of AI in MBS. Of the 28 statements evaluated, a consensus of at least 70% was achieved for all, with 25 statements reaching consensus in the first round and the remaining three in the second round. Experts agreed that AI has the potential to enhance the evaluation of surgical skills in MBS by providing objective, detailed assessments, enabling personalized feedback, and accelerating the learning curve. Most experts also recognized AI’s role in identifying qualified candidates for MBS referrals, helping patient and procedure selection, and addressing specific clinical questions. However, concerns were raised about the potential overreliance on AI-generated recommendations. The consensus emphasized the need for ethical guidelines governing AI use and the inclusion of AI’s role in decision-making within the patient consent process. Furthermore, the results suggest that AI education should become an essential component of future surgical training. Advancements in AI-driven robotics and AI-integrated genomic applications were also identified as promising developments that could significantly shape the future of MBS

Effectiveness of motivational interviewing at improving oral health: a systematic review

OBJECTIVE : To analyze the effectiveness of motivational interviewing (MI) at improving oral health behaviors (oral hygiene habits, sugar consumption, dental services utilization or use of fluoride) and dental clinical outcomes (dental plaque, dental caries and periodontal status). METHODS : A systematic search of PubMed, LILACS, SciELO, PsyINFO, Cochrane and Google Scholar bibliographic databases was conducted looking for intervention studies that investigated MI as the main approach to improving the oral health outcomes investigated. RESULTS : Of the 78 articles found, ten met the inclusion criteria, all based on randomized controlled trials. Most studies (n = 8) assessed multiple outcomes. Five interventions assessed the impact of MI on oral health behaviors and nine on clinical outcomes (three on dental caries, six on dental plaque, four on gingivitis and three on periodontal pockets). Better quality of evidence was provided by studies that investigated dental caries, which also had the largest population samples. The evidence of the effect of MI on improving oral health outcomes is conflicting. Four studies reported positive effects of MI on oral health outcomes whereas another four showed null effect. In two interventions, the actual difference between groups was not reported or able to be recalculated. CONCLUSIONS : We found inconclusive effectiveness for most oral health outcomes. We need more and better designed and reported interventions to fully assess the impact of MI on oral health and understand the appropriate dosage for the counseling interventions