The unknown etiology of sarcoidosis with variable clinical features leads to delayed diagnosis and limited therapeutic strategies. Hence, exploring the latent mechanisms and constructing an accessible and reliable diagnostic model of sarcoidosis is vital for innovative therapeutic approaches to improve prognosis.This retrospective study analyzed transcriptomes from 11 independent sarcoidosis cohorts, comprising 313 patients and 400 healthy controls. The weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were performed to identify molecular biomarkers. Machine learning was employed to fit a diagnostic model. The potential pathogenesis and immune landscape were detected by bioinformatics tools.GBP1, LEF1, IFIT3, LRRN3, IFI44, LHFPL2, RTP4, CD27, EPHX2, and CXCL10 was further constructed in the training cohorts by the LASSO algorithm, which performed well in the four independent cohorts with the splendid AUCs ranging from 0.938 to 1.000. The findings were validated in seven independent publicly available gene expression datasets retrieved from whole blood, PBMC, alveolar lavage fluid cells, and lung tissue samples from patients with outstanding AUCs ranging from 0.728 to 0.972. Transcriptional signatures associated with sarcoidosis revealed a potential role of immune response in the development of the disease through bioinformatics analysis.A 10-gene signature SARDS consisting of Our study identified and validated molecular biomarkers for the diagnosis of sarcoidosis and constructed the diagnostic model SARDS to improve the accuracy of early diagnosis of the disease. Sarcoidosis is a systemic autoimmune disease characterized by non-caseous necrotizing epithelioid granulomas that can affect various organs and tissues such as the lung, eye, skin, heart, and nervous system, with a predominance in young and middle-aged people , 2. AbouWith the rapid advances in bioinformatics, the assessment of blood transcriptional signature may provide a fast, easily accessible, and convenient screening approach to identify potential molecular biomarkers for diagnosing disease and explore the latent pathogenesis and immunological characteristics to provide additional therapeutic perspectives for better individual treatment. Molecular biomarkers from the blood transcriptome are widely used for disease diagnosis and pathogenesis exploration. Several studies have documented that gene expression profiling of peripheral blood could be used as biomarkers in multisystem diseases and immune-related disorders –11, likeOur study collected 11 microarray cohorts of sarcoidosis patients from the Gene Expression Omnibus (GEO). Our study is committed to identifying specific gene profiles correlated with sarcoidosis through bioinformatics analysis and constructing a robust sarcoidosis diagnostic signature (SARDS). Additionally, the results might provide new insights into the pathogenesis, immune characteristics, and potential treatment options for sarcoidosis.http://www.ncbi.nlm.nih.gov/geo/) by searching “sarcoidosis.” The inclusion criteria were as follows: (i) The datasets contained total RNA gene expression microarray data; (ii) the datasets included sarcoidosis and normal samples: the samples can be one of the five forms, including whole blood, peripheral blood mononuclear cells (PBMC), bronchoalveolar lavage (BAL) cells, and lung tissue; and (iii) the datasets had five samples of both sarcoidosis and normal patients at least. The data processing procedure of the research was illustrated in the workflow  enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. The biological processes, cellular components, molecular functions, and relevant pathways of the genes in the key MEs were implemented via the “clusterProfiler” R package . AccordiP-adjusted value < 0.05 and |log2 fold change (logFC)| >1 as the threshold of DEGs. The intersection of the GSE83456 positively correlated module genes with the upregulated differentially expressed genes of GSE42826, GSE42830, and GSE42834 was believed to be the key upregulated genes of sarcoidosis. Similarly, the intersection of the GSE83456 negatively related module genes with the downregulated differentially expressed genes of GSE42826, GSE42830, and GSE42834 was thought to be the key downregulated genes of sarcoidosis.The differentially expressed genes (DEGs) between sarcoidosis and normal blood samples were screened via the “limma” R package. We set The least absolute shrinkage and selection operator (LASSO) was performed to obtain a robust diagnostic performance model. LASSO is a popular algorithm that is broadly utilized in medical studies –18. 10-fP-adjusted value < 0.01 as cutoff criteria.The normalized enrichment scores (NES) were calculated for sarcoidosis based on the diagnostic model scores on GO terms and KEGG pathway in the Molecular Signature Database (MSigDB) via all GO gene sets (c5.go.v7.4.symbols.gmt) and KEGG gene sets as Gene Symbols (c2.cp.kegg.v7.4.symbols.gmt), respectively. We set |NES| > 1.50, and Single sample GSEA (ssGSEA)  that gent-test. P-value < 0.05 was determined to be statistically significant.Data processing, statistical analysis, and plotting were carried out in the R 4.1.2 software. The correlation between two continuous variables was assessed using Pearson's correlation coefficient. Comparisons of categorical variables were done using the Chi-square test, while comparisons of continuous variables were done using the Wilcoxon rank-sum test or A total of 313 sarcoidosis patients from 11 public datasets were collected for further analysis . A totalThe GSE83456 dataset was used to identify the key MEs related to sarcoidosis. First, no outlier sample was removed based on the sample tree, and then totaling 110 samples, the top 5,000 genes were used for WGCNA. The soft-thresholding power was set to nine to fit the scale-free network ,B. SeconP < 0.0001) (P < 0.0001)  . The tur 0.0001) . Figure  0.0001) . The enrThe DEGs between sarcoidosis and normal patients were explored by the “limma” R package. In the GSE42826 dataset, 20 significantly upregulated genes and eight significantly downregulated genes were defined, shown as a volcano plot and heatmap in GBP1, LEF1, IFIT3, LRRN3, IFI44, LHFPL2, RTP4, CD27, EPHX2, and CXCL10 with the coefficients 0.244, −0.0925, 0.0855, −0.0732, −0.0703, 0.0292, 0.0149, −0.0131, −0.00522, and 0.000941, respectively. The SARDS was established with the following formula: SARDS score = 0.445 + 0.244 × Exp GBP1- 0.0925 × Exp LEF1- 0.0855 × Exp IFIT3- 0.0732 × Exp LRRN3- 0.0703 × Exp IFI44 + 0.0292 × Exp LHFPL2 + 0.0149 × Exp RTP4- 0.0131 × Exp CD27- 0.00522 × Exp EPHX2- 0.000941 × Exp CXCL10.First, the key regulated genes were screened to further model construction by the intersection of the genes in the key module of WGCNA and the significantly regulated genes in the three datasets. Fourteen key upregulated genes were screened through the intersection of the brown module genes of WGCNA and the significantly upregulated genes of the three datasets . LikewisROC curves were used to assess the diagnostic efficacy of SARDS in 11 cohorts. The GSE83456, as the training cohort, performed an excellent AUC of 1.00 . The GSEThe correlation between SARDS scores and gene expression was calculated for gene sequencing to detect latent mechanisms of sarcoidosis by applying GSEA. The most important GO terms and the KEGG pathways were displayed in the ridge plot ,B. Amongr = 0.680, P < 0.0001), macrophages , iDC , neutrophils , pDC , and eosinophils  were positively correlated with the SARDS score; the infiltration level of T cells , Tcm , TFH , CD8 T cells , B cells , and Th2 cells  were negatively associated with the SARDS score.Given that sarcoidosis is a systemic inflammatory disease of unknown mechanisms, it is essential to exploit the immune microenvironment of sarcoidosis patients. The ssGSEA algorithm was performed to estimate the infiltration abundance of 24 types of immune cells between sarcoidosis and normal patients. The heatmap and boxplot demonstrated the fraction and expression differences of 24 types of immune cells in the GSE83456 cohort ,B. It waThe diversity of clinical symptoms in sarcoidosis and the lack of a single reliable diagnostic criterion make prompt and accurate diagnosis challenging. In addition, the etiology and pathogenesis of sarcoidosis remain unknown, making treatment available considerably limited. Therefore, exploring the latent mechanisms and constructing an accessible and reliable diagnostic model of sarcoidosis is vital for innovative therapeutic approaches to improve prognosis.Our study was based on gene transcriptome analysis of 303 sarcoidosis samples and 400 normal controls. Two modules with the highest correlation to sarcoidosis were identified through WGCNA, namely brown and turquoise modules. The brown module, containing 493 genes, demonstrated that the defense response to the virus and innate immune response might play an essential role in the pathogenesis of sarcoidosis, which was consistent with the results of previous studies. Numerous immunological arguments had indicated that inadequate clearance of viral particles, accompanied by various immunodeficiencies, might be relevant to sarcoidosis disease . In patiGBP1, LEF1, IFIT3, LRRN3, IFI44, LHFPL2, RTP4, CD27, EPHX2, and CXCL10. GBP1, as an IFN-γ-related gene in whole blood gene expression, was independently and positively correlated with T-bet+ frequency in Th17 cells, as the expression of T-bet in Th17.0 cells might indicate the degree of granulomatous inflammation in sarcoidosis patients (LEF1) is one of the Hippo signaling pathway hub genes, and it has been suggested that macrophage proliferation is related to the downregulation of the Hippo signaling pathway  that expressed IFN-responsive genes (such as IFIT3) and secreted the cytokine CXC motif chemokine 10 (CXCL10), which regulated the proliferation and differentiation of satellite cells  B cells may be a biomarker for treatment with TNF-α blocking agents. In addition, we found that LRRN3, IFI44, LHFPL2, RTP4, and EPHX2 were all involved in diagnosing sarcoidosis, which might shed light on the mechanisms of sarcoidosis and provide potential biomarkers for diagnosis. Overall, results from 11 different cohorts of whole blood, PBMC, BAL cells, and lung tissue supported the diagnostic efficacy of the essential genes, with the splendid AUCs ranging from 0.938 to 1.000 in training datasets and ranging from 0.728 to 0.972 in validation datasets. He J et al. found that BATF2 and PDK4 could be used as diagnostic molecular markers for sarcoidosis through bioinformatics approaches in two cohorts , pDC, and Langerhans cells (LC). In the immune process of sarcoidosis, dendritic cells migrate to lymph nodes and participate in t-cell proliferation through t-cell receptors and costimulatory molecules . SubsequOverall, our research had limitations and strengths. The limitations of the study were that the cohorts in our study contained only diagnostic information lacking clinical aspects. Besides, the essential genes screened were not validated experimentally. Further studies need to be carried out to validate the value of the clinical application. However, it had the strength of a sufficiently large sample size of sarcoidosis, containing 313 patients and 400 healthy controls in 11 cohorts with four different sample sources. Validation in various sample sources and diverse cohorts compensated for experimental validation. In addition, advanced bioinformatics methods and machine learning algorithms reduce the impact of disease heterogeneity and confounding factors on diagnostic models. Collecting and analyzing circulating cells, indicative of pathogenic mechanisms and immune characteristics, are less invasive and less costly for new diagnostic tools.In summary, our study systematically identified a feasible and credible diagnostic signature (termed SARDS) comprising 10 essential molecular biomarkers for the diagnosis of sarcoidosis and validated its robustness and translation in multiple cohorts of different source types. The study also had significant implications in exploring the underlying pathogenesis and the immune landscape of sarcoidosis for innovative therapeutic strategies. Taken together, SARDS could be a promising tool to optimize the diagnosis and treatment of patients with sarcoidosis.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/MD, ZL, and ZC designed the research. MD and ZL performed data acquisition and data analysis. PL, YW, and YuZ assisted with data analysis. MD wrote this manuscript. SW, YoZ, MF, RW, HX, and YR edited and revised this manuscript. All authors read and approved the manuscript.This study was supported by the National Natural Science Foundation of China (U1904142 and 82170037) and the Medical Science and Technology Research Project of Henan Province (SBGJ202002043).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Sensors developed from nanomaterials are increasingly used in a variety of fields, from simple wearable or medical sensors to be used at home to monitor health, to more complicated sensors being used by border customs or aviation industries. In recent times, nanoparticle-based sensors have begun to revolutionize drug-detection techniques, mainly due to their affordability, ease of use and portability, compared to conventional chromatography techniques. Thin graphene layers provide a significantly high surface to weight ratio compared to other nanomaterials, a characteristic that has led to the design of more sensitive and reliable sensors. The exceptional properties of graphene coupled with its potential to be tuned to target specific molecules have made graphene-based sensors one of the most popular and well-researched sensing materials of the past two decades with applications in environmental monitoring, medical diagnostics, and industries. Here, we present a review of developments in the applications of graphene-based sensors in sensing drugs such as cocaine, morphine, methamphetamine, ketamine, tramadol and so forth in the past decade. We compare graphene sensors with other sensors developed from ultrathin two-dimensional materials, such as transition-metal dichalcogenides, hexagonal boron nitrate, and MXenes, to measure drugs directly and indirectly, in various samples. Over the years, two-dimensional (2D) materials have been deployed to monitor a vast range of analytes, across a variety of disciplines, but it can be argued that the properties of the 2D materials are of particular relevance to the detection of psychoactive drugs due to the common requirement for miniaturized systems that allow on-site, discreet drug monitoring. The requirement for accurate, portable drug testing is typically assumed to be of relevance to forensic work, but with potential drug analytes spanning legal, illegal and medical drug compounds the requirement is also very relevant to clinical settings and workplace drug testing, particularly for safety critical industries .Conventional methods of drug analysis such as chromatography , immunocHere, we examine graphene and some of the 2D materials which have shown promising applications in the electrochemical sensing of recreational drugs and discuss recent progress made with regard to those sensors.2-bonded carbon atoms with π–π interactions [Among the different nanostructures, two-dimensional graphene has drawn the most attention . This unractions ,15,16. Tractions ,17,18,19ractions ,20,21. Tractions .There are seven main categories of psychoactive drugs, narcotics, depressants, stimulants, hallucinogens, cannabis, anabolic steroids and inhalants, which are identified to be mainly used for nontherapeutic purposes . While gIllicit drugs based on their effects on the central nervous system can be classified into various groups, as explained earlier. Depressants reduce stimulation and slow down message transfer in the body by lowering brain activity . Most ofOpioid receptors are expressed by various cells, but mainly central and peripheral neurons. Studies have defined three main types of opioid receptors in the central nervous system, the mu, delta, and kappa receptors . OpioidsSeveral opioids share similar molecular structures to the popular pain medication morphine, often featuring a hydrophobic ring skeleton and a hydrophilic tertiary amine  Figure , but desDespite the similarities in their molecular structures, subtle differences in structure or changes in functional groups can affect drug potency. While morphine is an effective painkiller that is widely used, including for the long-term treatment of severe pain in cancer patients , its synWhen it comes to pharmaceutical and medical applications, the regular monitoring of these drugs is of the utmost importance as it can endanger the health of patients. At the same time, the accurate determination of morphine, codeine and heroin levels in biological samples is of vital interest in the control of drug abuse, clinical toxicology and forensic cases . For insThe mechanism of the electrochemical detection of opioids with molecular structures similar to morphine has been linked to the electroactivity of both phenolic and tertiary amine groups in their structures. In the mechanism that includes phenol ring oxidation, each molecule loses a single proton which eventually causes the dimerization of morphine , whereasThe very first study on graphene-based sensors for this class of chemicals was conducted in 2012, where a glassy carbon electrode was modified with graphene nanosheets and used to simultaneously measure morphine, heroin and noscapine simply based on their direct electrochemical oxidation. Three oxidation peaks appeared when a mixture of samples was tested, in the range between 0.3 and 0.9 V, where the first two belonged to morphine and noscapine and the third one represents the mixture of morphine and heroin. They used individual drug tests to identify the concentration of heroin at the third oxidation peak in the presence of the heroin and morphine mixture. They reported LoD values of 0.4, 0.5 and 0.2 μM for morphine, heroin and noscapine, respectively . Since tMohamed et al. were able to use 3D spongy adenine functionalized graphene as a sensor to measure paracetamol, codeine and caffeine simultaneously as each of these drugs developed a separate response (peak), at different potentials, in square wave voltammetry. A mixture of blood plasma and pain-relief tablets (Solpadeine) was used as an authentic sample, where the sensor was able to accurately measure the amount of added codeine in the real sample. The LoD of the sensor for codeine was reported to be 5.8 nM . While gUsing a reduced graphene oxide–palladium hybrid as a selective modification for the glassy carbon electrode to sense morphine was studied in 2014. The electrode generated oxidation peaks related to the phenolic groups of morphine. The sensor was also tested with dopamine, uric acid, and ascorbic acid, showing separate oxidation peaks for each one of them, and then it was used to differentiate morphine oxidation peaks from the other three samples in a human urine test, which led to a detection limit of 40 nM for morphine . However2SnO4 nanoparticles with graphene in 2016. They achieved a homogenous composite through the hydrothermal reaction and heating a mixture of Zn2SnO4 nanoparticles and graphene oxide at 180 °C for 2 h. Despite the similarities between morphine and codeine, their sensor was able to generate two separate oxidation peaks for each one of the drugs and could simultaneously detect both drugs [3O4@SiO2 composite as a morphine sensor with a linear range between 1 and 100 μM (LoD = 0.75 μM) using a differentiate pulse voltammetry system. They reported that the various sugars, salts and amino acids that were tested did not affect the response of the sensor to morphine, and ascorbic acid was the only interference they found; however, they suggested using ascorbic oxidase enzyme treatment to eliminate ascorbic acid in the samples and minimize its effects on the sensor [Bagheri et al. utilized a composite made of Znth drugs   and acetaminophen (LoD = 0.025 μM) in square-wave voltammetry. While the sensor had not been tested in the presence of other common interferences such a morphine and ascorbic acid, it allowed them to measure codeine in various real samples, with reasonable accuracy and precision, close to the high-performance liquid chromatography (HPLC) results [Other than morphine, applications of graphene–metal oxide electrodes have also been studied for the drug codeine. Afkhami et al. reported a graphene–CoFe results .3O4, and various ionic liquids, and then modified each one of the electrodes further with the help of sodium dodecyl sulfate and tested them with urine samples and pharmaceutical morphine tablets to evaluate sensor performance. Similar to previous samples, sensors other than morphine still showed oxidation peaks for other molecules including ascorbic acid, dopamine and terazosin hydrochloride. The ionic liquid combination with sodium dodecyl sulfate increased the intensity of the oxidation peaks and helped with peak separation. The LoD of the sensor in the urine sample was found to be 0.484 nM [In 2019, Atta et al. attempted to improve morphine sensors using ionic liquids along with a reduced graphene oxide and cobalt oxide mixture. The authors formed different electrodes by mixing graphene, Co0.484 nM . Another0.484 nM . The senA voltametric morphine immunosensor developed in 2017 based on gold-decorated graphene showed promise as a more sophisticated sensor. A gold-decorated graphene base was first covered with cysteamine and then morphine-selective antibodies were immobilized on the surface of the developed electrodes. This highly selective surface showed a selective range between 0.1 and 100 ng/mL, with a detection limit of 90 pg/mL . DespiteReduced graphene oxide was also used to modify a membrane potentiometric pholcodine detector. The sensor includes the ion-association complex of pholcodine and 5-nitrobarbiturate, where o-nitrophenyloctyl ether worked as a solvent mediator and rGO was utilized as a solid contact ion-to-electron transducer in the compartment of the sensor. The sensor was tested on several drugs and salts (cations), and while similar molecules, including morphine, ethylmorphine and codeine, were found to have some interfering effects, the sensor was considered to be selective towards pholcodine. The LoD of the sensor was 0.04 μg/mL .Nitrogen and sulfur co-doping has also been found to affect the charge densities around carbon atoms, resulting in an enhanced electrochemical sensitivity of graphene towards analytes such as morphine. It has been reported that using thiourea as a dopant on graphene, followed by chemical reflux and annealing, resulted in the co-doping of nitrogen and sulfur atoms on the surface of graphene at different weight percentages. Doped atoms affect the charge densities around carbon atoms, resulting in an enhanced electrochemical performance and detection of morphine. The authors suggested that increasing dopant concentrations prompts a higher electrocatalytic response towards morphine. Their co-doped graphene composite was used for the voltametric detection of morphine, which offered two separate linear ranges for higher concentrations (294–9.81 μg/mL) and low concentrations (4.59–0.574 μg/mL) and a LoD of 0.26 μg/mL. They successfully detected morphine in real soft drink, beer and vodka samples and suggested that the synergistic effect of the co-doping of N and S atoms on the graphene substrates was responsible for the sensitivity of their sensor .Tramadol is a member of the phenanthrene opium alkaloids , and unl2O4 and graphene made one of the first nanocomposites that was used to measure tramadol electrochemically. The sensor was also able to measure acetaminophen as well, simultaneously in square-wave voltammetry. Metal oxide nanoparticles (NiFe2O4) were reported to enhance the sensitivity of the sensor, enabling a LoD of 3.6 nM for tramadol and 3 nM for acetaminophen. The sensor was tested to measure tramadol in urine, human serum, tramadol tablets and Ultracet tablets, where its measured concentrations matched the actual drug levels added in each sample and HPLC results [Mohamed et al. compared the response of an unmodified carbon paste electrode (CPE) before and after the addition of a composite made of graphene oxide and multi-walled carbon nanotubes. They reported that the addition of GO-MWNTs improved the electroanalytical characteristics of the electrode and increased sensitivity towards tramadol, with a linear range between 2 nM and 1.1 mM . This pe results . Gold-de results .3O4. The developed sensor was able to detect tramadol in urine and in tablets with slight false positive error in a linear range between 0.1 and 500.0 μM, and with a LoD of 0.03 μM [Further work in 2020 explored the applications of metal oxides for the detection of tramadol by linear sweep voltammetry, differential pulse voltammetry, cyclic voltammetry, and chronoamperometry. Decorated nanoparticles were obtained through a hydrothermal reaction, where cobalt salt and graphene oxide first went through a heating process in an autoclave in the presence of hydrogen peroxide and were washed with water, before going through another hydrothermal reaction in the presence of sodium borohydride, to finally form graphene–Co 0.03 μM .Bagheri et al. used silver-decorated graphene as the base for tramadol-selective molecularly imprinted polymers. The authors decorated graphene layers with silver using silver nitrate and sodium borohydride. The reducing agent (sodium borohydride) was responsible for reducing the graphene oxide and formation of silver nanoparticles. Molecularly imprinted polymers were developed by polymerizing a monomer in the presence of analyte and then the analyte was washed away to leave the developed selective frames ready to interact with the analyte in various samples. The sensor was casted using a paste made of decorated graphene; selective polymer frames and ionic liquids were used as conductive binder. The sensor was successful in accurately detecting tramadol in pharmaceutical tablets. The LoD of this potentiometric sensor was reported to be 2.09 nM .A nanocomposite developed from graphene quantum dots modified with both praseodymium hydroxide nanoparticles and imidazolium ionic liquid has also been reported to be sensitive and selective towards low concentrations of tramadol. Cyclic voltammetry studies of tramadol with the developed electrochemical sensor showed the appearance of an irreversible anodic oxidation peak, where the intensity of the peak has a linear correlation with the concentration of tramadol in a wide range between 9 nM and 0.3 mM. The practical application of the sensor was tested and confirmed with tramadol medical injection vial and spiked blood serum. The detection limit of the sensor was found to be 3 nM. However, the electro-oxidation of tramadol and sensitivity of the sensor was reported to be highly affected by pH values .Methadone is a synthetic analgesic drug which has been utilized for treating opioid dependence for over half a century . It has Buprenorphine is a narcotic analgesic that acts similarly to methadone in terms of its action and utilization . In a siKetamine is another analgesic drug with wide application in the medical field ,70. HoweThe idea of using a microfluidic paper-based analytical device with a chip developed from zeolites nanoflakes and graphene oxide (Zeo–GO) was one of the first studies in the development of a graphene-based electrochemical sensor for this date rape drug that achieved a detection limit of 1 nM and showed application in both alcoholic (whisky) and nonalcoholic (juice) drinks . The useMolecularly imprinted polymers have also been used for ketamine-selective functionalization for graphene-based sensors. In 2019, Fu et al. suggested using a molecularly imprinted membrane on a graphene layer which was previously modified with metal–organic frameworks in compartments of an electrochemical sensor to detect and measure ketamine levels in biological fluids. Firstly, they developed a metal–organic framework (MOF) by a solvothermal reaction using 1,4-benzendicarboxylicacid and zirconium metal salt. A paste was made of graphene and Zr-MOF made in ethanol and used to cast the base of the sensor. To make the selective membrane, monomers were mixed with ketamine and the mixture was dropped onto the surface of the casted electrode to form a thin layer. The polymerization of monomers started when the developed surface was exposed to UV light, and ketamine molecules were later simply washed from the polymer membrane surface with methanol and acetic acid. Cyclic voltammetry was utilized to measure the response of the electrode to ketamine in various samples. The sensor showed selective characteristics towards ketamine and its metabolite norketamine, as it did not respond to methylenedioxymethamphetamine, methylamphetamine, dopamine, and ascorbic acid. The selective membrane let the electrode selectively and accurately measure ketamine in urine and saliva. The linear range of the sensor was 0.1 nM to 0.4 μM, with a LoD of about 0.04 nM .Stimulants function in a manner opposite to depressant drugs and mainly accelerate brain activity. They increase the mood of the user and make them feel energetic. This class of drugs are highly reinforcing, as they directly affect brain systems implicated in motivated behavior, such as the basal ganglia and the limbic system, and they modulate control systems in the prefrontal cortex ,78,79.Cocaine and amphetamines are among the most abused and powerful stimulants , and botCocaine has strong potential for addiction and illegal abuse. The sensitive and accurate detection of cocaine in various samples is therefore of critical importance, not only for clinical diagnostics, but also for legal and forensic investigations ,81,82. DWhile a few papers studied the applications of graphene for developing a selective fluorescence sensor to measure cocaine ,84, KarlAnother cocaine-selective electrode was developed by utilizing molecularly imprinted polymers on the surface of palladium-decorated graphene. While codeine itself was used as the template molecule to develop selective p-aminobenzoic-acid-based polymer frames through electrodeposition, palladium decoration was applied to enhance the communication between the imprinted polymer and graphene. Square-wave voltammetry was used for cocaine quantification with this sensor, and showed a linear response in the range of 100–500 µM, with a detection limit of 50 µM. Real street samples of cocaine, saliva (spiked cocaine) and river water (spiked cocaine) were studied as authentic samples and the sensor was able to detect and measure drug levels close to the spiked levels .Rocha et al. developed a 3D-printed graphene–polylactic acid electrode to measure low concentrations of cocaine in various samples with a simple sample pretreatment step (electrolyte addition) by utilizing square-wave voltammetry . They usMethamphetamine is another powerful central nervous system (CNS) stimulant that is widely abused. This drug also has medicinal applications such as the treatment of patients with obesity or drinking problems . Its unc2 laser to induce 3D porous graphene on it. The method was solvent free and allowed them to fabricate various designs. Their flexible sensor was then tested with a portable potentiometer in a cyclic voltammetry system, where it was shown to have a LoD of 0.3 μg/mL. To investigate selectivity, they tested the sensor’s response versus sugars, salts (cations and anions), uric acid, urea and ascorbic acid, and they reported a highly selective response for methamphetamine  to be analyzed with cyclic voltammetry. The LoD of the sensor was found to be 1 nM with a linear range between 0.005 and 200 μM. The selectivity of the sensor was not studied compared to other drugs, though the sensor did not respond to tested amino acids and salts [2O3-decorated graphene was then placed and dried on the surface of the glassy carbon electrode. Samples were studied with this electrode using square-wave voltammetry. The addition of drugs such as acetaminophen, morphine, tramadol and cocaine, metal salts, uric acid, dopamine, glucose, and ascorbic acid had an insignificant effect on the response of the sensor to methamphetamine at the studied peak potential. The minimum concentration of methamphetamine that the sensor was able to detect was 8.7 μM [Another methamphetamine-selective electrochemical sensor fabricated by Riahifar et al. also used reduced graphene oxide as the base of the sensor, but with further functionalization. First, graphene oxide was reduced on the surface of a glassy carbon electrode. Then, iron chloride was turned to iron oxide in the presence of ammonium hydroxide, followed by polymerization in a mixture with pyrrol monomers, resulting in conductive polypyrrol forming a shell around the Fend salts   and very thin layers of black phosphorus and phosphorene. First, we briefly review their characteristics before discussing their sensing applications.Thin, layered materials, similar to graphene, that have been reported and studied for their applications in developing electrochemical sensors to measure dopamine include transition metal dichalcogenides (TMDs), MXenes, hexagonal boron nitride  instead of common elements such as carbon, nitrogen, etc. (X) [h-BN) has shown excellent electrocatalytic activity, offering active nitrogen and boron at edges, defects similar to graphene, and an adjustable band gap which can be modified with the help of other nanoparticles or chemical functionalization. h-BN has proved to be another ideal thin material for electrochemical sensing applications [Transition metal-based 2D materials can be categorized into five subcategories based on their molecular structures . They caructures ,103,104.etc. (X) ,106. Whietc. (X) , ultrathetc. (X) ; these cications .2V−1s−1 was obtained from the field effect transistors based on phosphorene on Si, which suggested that it could be further enhanced to 2000–4000 cm2V−1s−1 when it is located on the h-BN substrate or between two h-BN flakes at low temperature [The pursuit for the discovery and development of 2D materials, similar to graphene, caused rapid growth on both theoretical and experimental fronts, which led to the recent discovery of a single 2D layer of BP called phosphorene ,111 in 2perature ,118,119.perature ,122,123 perature .h-BN-based sensors the LoD can be as low as 2 × 10−13 molar [As expected from 2D-based sensors, in , we can 13 molar . TMDs anTraditionally, the majority of drug-detection methods using analytical systems are undertaken using chromatography-based techniques (gas chromatography (GC) and HPLC), spectroscopy techniques (refractive index (RI), UV-Vis, fluorescence, evaporative light scattering, and NMR), or hyphenated approaches (GC-MS and HPLC-MS). Each of these techniques has their own advantages and disadvantages, but among them, three systems are more popular, especially when it comes to drug analysis: UV, fluorescence and MS ,140,141.Graphene or graphene oxide have been applied as sensitive drug sensors, often without significant functionalization, due to the ability of each molecule to form oxidation or reduction peaks when studied with cyclic or square-wave voltammetry. Whilst many studies have reported such applications, it is apparent that for the technique to become more commercially viable the selectivity of these sensors needs further demonstration. Many of the reportedly selective drug sensors have only been tested against simple cations, anions, amino acids and small biological molecules, and so it is not clear how those sensors would respond in the presence of other drugs or large molecules, especially as many of the drugs often have similar molecular structures. In the case of monitoring street drugs, such compounds usually do not have high purity and there is a need to monitor drugs on potentially contaminated surfaces and in complex matrices such as biological fluids. The papers reviewed here clearly demonstrate the potential of graphene to be used in the development of selective electrochemical sensors for drug targets, but the selectivity of sensors needs to improve further as the reliability of sensors with this application is a must for pharmaceutical and forensic monitoring.Graphene alone has been shown to improve the sensitivity of sensors and the decoration of graphene with metal or metal oxides can improve sensitivity even further. The development of selective receptors seems to be challenging and biosensors seem to be one of the most selective options available. However, due to the difficulties associated with biosensors, such as their short lifetimes, a viable alternative may be molecularly imprinted sensors. A promising area for this field to develop into may be the development of chemical functional groups, anchored to the surface of graphene, to interact with large drug molecules. This is currently an under-researched area of graphene-based sensors. Anchoring functional groups to graphene, instead of using of polymers, has the potential to improve the conductivity, resulting in faster, more sensitive drug sensors. As expected, and illustrated in some of the discussed papers, CV, SWV and DPV were the most common methods of studying the behavior of electrochemical sensors; pulse techniques, such as DPV, are more sensitive than linear sweep methods due to the minimization of the capacitive current and sensors were able to detect and measure even smaller changes.Graphene-based electrochemical sensors targeting depressants such as morphine have been widely studied, but the use of graphene in the detection of other classes of drugs such as stimulants or hallucinogens, and especially the recent focus on novel psychoactive substances, is a less studied field, with high potential for publication. If we consider the potential of other 2D materials for application as drug sensors, the potential is equally high. The large surface area and high electrical conductivity of such materials provides excellent possibilities for future development in this field, with real potential for academic and commercial impacts.


Advanced metallurgical processing techniques are required to produce aluminum matrix composites due to the tendency of the reinforcement particles to agglomerate. In this study, graphene nano-platelet reinforcement particles were effectively incorporated into an automotive A319 aluminum alloy matrix using a liquid metallurgical route. Due to its low density, it is a highly difficult task to produce an aluminum matrix composite reinforced with graphene. Hence, this study explored a novel approach to prevent particle floating to the melt surface and agglomeration. This was achieved via a hybrid semi-solid stirring of A319, followed by ultrasonic treatment of the liquid melt using a sonication probe. The microstructure and graphene particles were characterized using optical microscopy and scanning electron microscopy. Furthermore, the interfacial products produced with the incorporation of graphene in liquid aluminum were analyzed with X-ray diffraction. The tensile test results exhibited 10, 11 and 32% improvements in ultimate tensile strength, yield strength, and ductility of A319 reinforced with 0.05 wt.% addition of graphene. Analysis of strengthening models demonstrated primary contribution from Hall-Petch followed by CTE mismatch and load bearing mechanism. The results from this research enable the potential for using cost-effective, efficient and simple liquid metallurgy methods to produce aluminum reinforced graphene composites with improved mechanical properties. These reinforcements have been found to increase the tensile strength, hardness, density and wear resistance of Al and its alloys [High-strength aluminum (Al) alloys, such as the A319 alloy, are commonly used in the production of lightweight and high-performance automotive powertrain components. Continuous efforts are made by researchers to improve the properties of Al alloys including through the use of modification, grain refinement and heat treatment. An alternative strengthening method is through the reinforcement of metals with hard ceramic particles has gained impetus to produce metal matrix composites (MMCs). MMCs are manufactured by dispersing the reinforcements in the metal matrix. The reinforcements are typically applied to improve the properties of the base metal such as strength, ductility, and thermal conductivity. Aluminum and its alloys have attracted much attention as a base metal in MMCs . Aluminu2 hybridized carbon atoms) and significantly enhanced mechanical and thermal properties [In recent years, graphene has emerged as a material with high potential as a reinforcement particle for AMCs. Graphene is an atomic-scale honeycomb lattice made of carbon atoms. It has unique properties such as high strength and thermal conductivity ,5. Graphoperties .2/g) and low density of graphene (~1.8–2.2 g/cm3) compared to Al (~2.7 g/cm3). As a result, various processing techniques have been explored to determine the optimal method for incorporating graphene into Al.The particle distribution plays a very vital role in the properties of the AMCs, which is improved by intensive shearing of the liquid melt or rigorous mixing of metallic powders. As a result, appropriate processing of AMCs reinforced with graphene is key in developing a material with enhanced properties. Optimized dispersion of graphene reinforcement in the Al matrix plays a critical role in improving the properties of AMCs. This is difficult to achieve because of the strong van der Waals forces between graphene particles, high specific surface area  in A319 Al alloy for automotive cylinder heads and engine blocks. To the authors’ knowledge, reinforcement of automotive A319 Al alloy with graphene has not been comprehensively investigated. In contrast to past works, this study demonstrates the isolated effects of semi-solid stirring and direct-contact ultrasonic stirring, as well as the combined effects. Furthermore, the results reveal the individual strengthening contribution of graphene, without the aid of a secondary reinforcement particle. The use of pre-processing techniques, such as ball-milling, were also not required to incorporate graphene in the liquid melt. Most studies involve the use of preprocessing methods such as ball-milling to first manufacture a master-alloy composite prior to casting. This has been found to be successful, although they typically increase the manufacturing time and cost. While there has been significant progress in research involving the incorporation of graphene as a reinforcement particle for Al using liquid metallurgy processes, there is still potential for improvement especially when considering automotive applications as the industry moves towards sustainable electric vehicles.In this study, a simple and innovative, hybrid semi-solid stirring and ultrasonic processing is implemented to produce an A319/GNP reinforced composite with improved mechanical properties without the need for complex preprocessing methods. A comprehensive analysis on the individual effects of semi-solid stirring and ultrasonic processing on A319 was investigated. Subsequently, the combined effects were then explored and compared to the individual processes. This was achieved using optical microscopy, scanning electron microscopy, X-ray diffraction and tensile tests. The strengthening mechanisms were also studied using existing mathematical models and validated experimentally. The results from this research enable the potential for using cost effective, efficient and simple liquid metallurgy methods to produce aluminum reinforced graphene composites with improved mechanical properties.Castings of A319-graphene MMCs were produced at the Centre for Near-net-shape Processing of Materials, Toronto, ON, Canada. Graphene particles (graphene nano-platelets) were obtained from XGSciences, Lansing, MI, USA for the production of high-strength GNP reinforced MMCs. The GNPs had an average diameter of 1 µm and thickness of 15 nm, according to the supplier data sheets. In this study, five experimental casting conditions were used and they involved semi-solid processing (SS) or ultrasonic processing (UST). The material designations for the experimental conditions are summarized in For each casting, approximately 1.5 kg of the alloy was melted at 750 °C in a SiC crucible using an electric resistance furnace. Subsequently, a ASTM B108-19 permanent tensile mold was preheated to 300 °C. A combination of semi-solid stirring and ultrasonic stirring was used to incorporate GNPs in the A319 melt. Once the alloy was molten, the furnace was cooled to 590 °C and held at this temperature to allow the A319 alloy to reach semi-solid state. This corresponds to a 0.2 solid fraction for the A319 alloy . SubsequAfter casting, tensile testing was performed on samples using a Instron United Universal Testing Machine Model STM-50kN  according to ASTM B 557M-15. Tensile tests on the dog bone shaped samples (12.95 mm diameter and 50.8 mm gauge length) were carried out with a cross head speed of 12 mm/min at ambient temperature. An extensometer was used to measure strain. After tensile testing, the samples were sectioned for fractography and microstructural analysis. Prior to metallographic preparation, the density of each of the casting conditions was measured using the Archimedes principle on the sectioned samples.α radiation at 40 mA and 45 kV.A Nikon Eclipse MA200  metallurgical optical microscope was used to analyze the as-cast grain structure. The samples were prepared for metallography by successive grinding steps using varying levels of SiC papers and subsequently polished using 5 µm alumina, 3 µm diamond suspension, 1 µm diamond suspension and 0.02 µm colloidal silica. The secondary dendrite arm spacing (SDAS) was measured using image analysis with Clemex Vision Pro software  and the linear intercept method. Furthermore, the GNP particles were examined using a JEOL JSM-6380LV scanning electron microscope (SEM) at 20 keV. Phase microanalysis was carried out using energy dispersive X-ray spectroscopy (EDX). The secondary phases and interfacial reaction products were identified by X-ray diffraction . The samples were scanned at 0.05° for 2 s using monochromated Cu K2Cu, and Al153Si2 phases which were distributed within the interdendritic regions. The optical micrographs of the base A319 alloy can be seen in 2Cu phases were observed in both blocky and lamellar morphologies.Optical microscopy and scanning electron microscopy were used to examine the as-cast grain structure and secondary phases present in the matrix. The grain structure of the castings was found to be dendritic. Furthermore, the as-cast microstructure of the A319 Al alloy consists of α-Al, eutectic Si, θ-AlThe SDAS decreased slightly with 0.05 wt.% GNP addition (A319 + 0.05 GNP + SS + UST) to 16.0 ± 0.8 µm compared to 19.8 ± 2.4 µm with the base A319 casting . This re2Cu and Fe-bearing phases. Eutectic Si was also present, as previously shown in 2Cu phases were homogeneously distributed, however they appear to be slightly fragmented as micro-spherical forms of Al2Cu are present near the larger blocky phases. This is may be a result of the sonication effect, which is known to refine secondary phases due to cavitation induced by high frequency vibration.Casting experiments were performed to ensure that GNPs were embedded into A319 Al alloy for the production of A319 reinforced GNP composites. For this purpose, SEM analysis was carried out on the samples containing GNPs. Additionally, GNP particles were observed in the matrix of the A319 Al alloy using SEM as seen in 4C3. The high temperature processing, coupled with a relatively long processing time promotes breaking down of C atoms from graphene. Hence, it is thermodynamically favorable that a reaction can occur between C and available Al in the melt to produce Al4C3. X-ray diffraction was implemented to confirm the presence of Al4C3 with the addition of GNPs  closely overlaps with the Al153Si2 secondary phase, while it is possible for the Al4C3 interfacial product to reduce the mechanical properties, this was not observed in this study.The addition of GNPs can also promote the formation of interfacial products such as Al of GNPs , since id at 43° b. Compar and 43° . Similar and 43°  where thIn this study, tensile testing was carried out to determine the mechanical properties of the composite castings. The mechanical properties of the casting conditions are shown in In addition to the findings on the UTS, the yield strength was also the highest for the A319 + 0.05 GNP + SS + UST samples. The YS for these samples was measured at approximately 119 MPa compared to the base A319 cast samples, which demonstrated a YS of 107 MPa. As shown in The results suggest that while semi-solid processing can prevent floating of GNP particles to the melt surface, the process is not effective in de-agglomerating the GNP particles. Hence, ultrasonic processing is required de-agglomerate and distribute GNPs in the melt. As demonstrated by the UTS and YS results, the combination of ultrasonic processing and semi-solid processing demonstrates potential for producing AMCs reinforced with graphene with improved mechanical properties. In summary, the tensile test results demonstrated 10, 11 and 32% improvements in UTS, YS, and ductility of A319 reinforced with 0.05 wt.% addition of graphene through using hybrid semi-solid and ultrasonic processing.In this study, theoretical models were used to determine the mechanisms for enhancing YS of the A319 + 0.05 GNP + SS + UST composite. A modified shear-lag and enhanced dislocation density model, developed by Fadavi Boostani et al.  was used0.5) [Equation (2) is composed of the strengthening effects in the form of load bearing (Pa/M0.5) . Using ti et al. .The load transfer from the Al matrix to graphene particles is known to contribute to the strengthening of the matrix material, this phenomenon can be expressed by Equation (3). This model is modified to account for the platelet-like morphology of graphene sheets or graphene nano-platelets. Hence, the load bearing parameter is defined as:There is a substantial difference in the coefficient of thermal expansion (CTE) of graphene and aluminum. This leads to an increase in dislocation density as a result of a plastic strain developed from the large ΔCTE. This can be expressed by Equation (5):Inserting the values Fractography using SEM was carried out after tensile testing the cast samples. This study explores a processing route using a liquid metallurgy method to produce a A319 graphene reinforced MMC. The purpose of this study is to determine a process to effectively insert GNPs in automotive A319 Al alloy through liquid metallurgy, specifically casting. The results demonstrate that ultrasonic processing and semi-solid processing individually cannot effectively distribute GNPs in the liquid melt. Hence, a combination of both ultrasonic processing and semi-solid processing was performed to achieve effective addition of GNP particles. In order to demonstrate the effectiveness of combining both methods, casting conditions that isolate each process and the base condition were carried out. As a result, an A319-0.05 wt.% GNP composite (A319 + 0.05 GNP + SS + UST) was successfully produced with improved mechanical properties. This was achieved via a simple and innovative hybrid semi-solid and ultrasonic processing method. In order to demonstrate the synergistic effect of the hybrid method, castings of base A319, A319 with UST, and A319 with SS processing were also produced. Hence, these conditions demonstrate the isolated effects of UST and SS while also comparing to the A319 alloy without additions.The SDAS measurements of A319, A319 + UST, A319 + 0.05 GNP + SS, and A319 + 0.1 GNP + SS were measured as 19.8 ± 2.4, 21.8 ± 2.0, 22.2 ± 0.5, and 20.8 ± 0.6 µm, respectively . BetweenSEM analysis was carried out for observation of GNPs and analysis of the GNP interface with the Al matrix. The results showed that GNP particles were embedded in the Al matrix, typically observed near eutectic Si particles. This may indicate that during solidification graphene particles were pushed towards the interdendritic regions, as opposed to pinning at the boundaries of the dendrites. As previously discussed, this suggests that it is related to solidification mechanisms which are affected by the high thermal conductivity of graphene. Additionally, SEM and EDX confirmed the platelet morphology of the observed particle as well as the chemistry, demonstrated in 4C3 can form if the GNP particles break down during the processing. This also is more likely to occur with increased processing times. Because preparation of the A319 + 0.05 GNP + SS + UST composite involves a multi-stage melt treatment, the processing time was longer compared to a conventional casting process. 2Cu, Si and Al153Si2. However, the results demonstrated that at 43° there was a peak, likely corresponding to Al4C3. Despite this, it was difficult to identify using XRD and the corresponding database likely due to the low addition of GNPs. The results correlate well to a study by Lee et al. [4C3 in Al-Si alloys, where the authors determined that the transition temperature for the formation of Al4C3 is approximately 620 °C. In this study, the processing temperature reached a maximum of 750 °C, hence it is thermodynamically favorable for Al4C3 to form during the high temperature processing. Of note, it is likely that the amount of Al4C3 is minimal due to the low addition of 0.05 wt.% GNPs. Moreover, localized inhomogeneity and variation in composition, likely resulted in the interesting observation of Al4C3 in limited quantity. The direct observation of Al4C3 will be carried out using high-resolution transmission electron microscopy (HR-TEM) as part of a separate on-going study by the authors. Lastly, the limited presence of Al4C3 was not found to deteriorate the mechanical properties of the A319 + 0.05 GNP + SS + UST composite.With the addition of reinforcing particles such as GNPs, it is also important to consider the interfacial interaction between the reinforcement particle and the matrix. The addition of reinforcement particles at high processing temperatures may produce reaction products, as the thermodynamic stability becomes weaker with elevated temperatures. Namely, Ale et al.  on the fIn this study, the mechanical properties were evaluated for each of the castings . The resAs previously discussed, GNP particles were observed under the SEM embedded in the Al matrix. While GNPs are a contributor to the strength enhancement of A319, it should be noted that the sonication process also degasses the melt. Hence, there is an additional benefit of ultrasonic processing when producing a composite through casting. Interestingly, ultrasonic processing alone was found to exhibit slight improvements in the mechanical properties of A319, where the UTS and YS slightly increased from 190 to 195 MPa and 107 to 109 MPa, respectively. As previously mentioned, the slight improvement in mechanical properties is due to degassing of the melt. Although, it was found that UST processing did not refine the A319 alloy, which has been reported to occur in other alloys such as Mg ,28.In order to isolate the effects of semi-solid processing, castings involving the addition of GNPs were carried out only using semi-solid processing as the melt treatment. For both the A319 + 0.05 GNP + SS and A319 + 0.1 GNP + SS composites, the UTS decreased by 5.3 and 7.4%, respectively. Similarly, the fracture behavior was brittle in both castings with the worst condition (A319 + 0.1 GNP + SS) having a ductility of 1.9%. This was a result of severe agglomeration of the GNP particles, which can be seen in The strengthening contributions were also analyzed using theoretical mathematical models. The main strengthening contributors to YS are Hall-Petch, load bearing mechanism, and thermally induced dislocations as a result of CTE mismatch between graphene particles and Al. The theoretical YS enhancement from each mechanism was calculated to be 16.2, 3.0 and 4.8 MPa, respectively. Hence, the highest contribution to strength was due to the dendritic grain refinement through the Hall-Petch relationship. This is followed by CTE mismatch between graphene and the Al matrix, then by load bearing mechanism. As a result, the total contribution to YS enhancement is 24 MPa resulting in a theoretical YS of approximately 130 MPa. Although, compared to the experimental result there is a deviation by approximately 8.5%. Hence, it can be suggested that the model can be considered to predict the YS of AMCs reinforced with GNPs.4C3, is also not accounted for in the theoretical models. It can be expected that such compounds can act as either a strengthening phase or deteriorate the YS.The discrepancy in the YS between the theoretical calculation and the experimental result can be attributed to some factors. The total calculated volume fraction does not fully represent that of the experiment due to losses throughout the casting process. The orientation of GNPs is not accounted for in the model, for example the strength properties are more dominant in the transverse loading direction compared to parallel with the corresponding plane. Furthermore, it is not fully known which planes the GNPs will inhabit during solidification, although it is expected that to achieve optimal strengthening enhancement this should be the (111) aluminum slip plane. The formation of interfacial products, such as Al4C3 was minimal with the addition of 0.05 wt.% GNPs. This was an important finding due to the negative effect of Al4C3 on the mechanical properties. Lastly, it was found that theoretical strengthening models resulted in values close to the experimental results, although adjustments should be made to account for the orientation of GNPs in the metal matrix.The results of this study demonstrated the effectiveness of reinforcing A319 with GNPs through a simple liquid metallurgy technique. To the authors’ knowledge, reinforcement of automotive A319 Al alloy with graphene has not been comprehensively investigated. As a result, the findings in this study further contributes to light weight and development of high-strength materials for automotive powertrain components. The mechanical properties of A319 were enhanced with cost effective and low additions of GNPs using a hybrid semi-solid and ultrasonic melt treatment during the casting process. As a result, the formation of AlThis study is significant since it clearly demonstrated that high cost and time-consuming preprocessing methods such as ball milling, spark plasma sintering and other methods are not required for efficient production of GNP reinforced Al composites. This is especially important for developing high strength materials for the automotive industry. Therefore, cost effective and efficient cast Al-GNP composites prepared through liquid metallurgy techniques, such as casting, can be used for next generation automotive vehicles leading to light weight and increased performance.The combination of semi-solid processing and ultrasonic processing was an effective and efficient method for the incorporation of GNPs in A319 Al alloy. This was attributed to prevention of particle floating during semi-solid stirring and the ability of ultrasonic processing to effectively de-agglomerate and distribute the GNPs in the liquid melt.Semi-solid processing of A319 Al enabled the prevention of floating of GNPs to the melt surface, however it was ineffective in de-agglomerating GNP particles. This resulted in excessive agglomeration of GNPs promoting a brittle fracture and a reduction in mechanical properties.Addition of 0.05 wt.% GNPs to A319 Al using the hybrid semi-solid and ultrasonic processing method resulted in an enhancement in UTS, YS, and ductility by 10, 11 and 32%, respectively. The SEM results revealed good interfacial bonding between GNP and Al-matrix. This was an important finding due to the very low addition of GNPs, which suggests a cost-effective incorporation of particles accompanied by the enhancement in mechanical properties.4C3 with the addition of GNPs, which did not lead to any reduction in mechanical properties in the A319 + 0.05 GNP + SS + UST composite. This was attributed to localized inhomogeneity and variation in composition, likely resulted in the interesting observation of Al4C3 in limited quantity.X-ray diffraction results indicate the limited presence of AlThe strengthening contributions for the A319 alloy reinforced with 0.05 wt.% GNPs (A319 + 0.05 GNP + SS + UST) were evaluated using mathematical models. The strength enhancements were primarily attributed to the Hall-Petch effect, followed by CTE mismatch and load bearing mechanism.The mathematical models used to estimate the YS of the alloy corresponded well to the experimental results. The theoretical YS was calculated as 130 MPa, whereas the experimental YS was measured as 119 MPa. Thus, there is an 8.5% deviation and it can be suggested that the model is accurate in predicting YS of aluminum matrix composites reinforced with GNPs.This research investigated a processing route using a casting process to produce a A319 graphene reinforced MMC. As a result, an A319-0.05 wt.% GNP composite (A319 + 0.05 GNP + SS + UST) was successfully cast with improved mechanical properties. This was achieved via a hybrid semi-solid and ultrasonic treatment method. In order to demonstrate the synergistic effect of the hybrid method, castings of base A319, A319 with UST, and A319 with SS processing were also produced. The alloy microstructure, analysis of graphene in the Al matrix, and the mechanical properties of the castings were examined. Subsequently, the strengthening contributions were also analyzed using theoretical mathematical models, which were then validated with the experimental results. The results from this research enable further understanding of a simple liquid metal processing route to produce aluminum matrix composites reinforced with graphene for high performance next generation automotive vehicles. The following conclusions can be drawn from this study:


The trade-off between short-term success and long-term sustainability is a common subject of great importance both in the biological evolution of organisms and in the economic activities of human beings. In evolutionary biology, bet-hedging theories have described it as the trade-off between the (within-generation) arithmetic mean fitness and the (between-generation) geometric mean fitness of a genotype. Accordingly, bet-hedging strategies observed in various organisms are regarded as optimizing the geometric mean fitness. To increase the geometric mean fitness signifies to suppress the between-generation variance in the mean fitness. Thus, this view is consistent with mean-variance portfolio analysis in which the standard deviation of a portfolio is regarded as a measure of risk. In the present study, we provide yet another measure of long-term sustainability, which is based on minimization of the probability of extinction/bankruptcy that randomly varying population/asset size after a long time becomes less than a certain small value. We present results for representative examples to show that the present criterion gives a qualitatively similar but quantitatively different prediction from the traditional ones. In particular, we emphasize that maximizing survival probability (i.e. minimizing extinction probability) is equivalent neither to maximizing geometric mean fitness nor to minimizing variance in mean fitness, while these three are consistently related to each other. Significance StatementDiversification by risk spreading plays a key role in sustainable development in economy and ecology. In evolutionary ecology, either geometric-mean fitness or variance in fitness has been considered a long-term measure to deal with the trade-off between short-term success and long-term sustainability. This paper presents yet another measure based on the probability of extinction after a sufficiently long time. The present criterion gives a qualitatively similar result supporting bet-hedging strategies but a quantitatively different result for optimal diversification.The trade-off between short- and long-term successes is of essential importance in evolutionary ecology and sustainable economy, i.e. in the survival strategy of biological organisms and in the investment strategy of financial investors. In evolutionary biology, this trade-off between the immediate success in the next generation and the long-term survival of its lineage is of primary concern in what is called bet-hedging . Indeed,However, there is an important difference between biological and economical bet-hedging. The trade-off in investment is left up to the discretion of investors. Biological systems have no agent of discretion to resolve the trade-off. In fact, biological bet-hedging is a result of the long-term success of organisms in the environment in which they evolved. To bridge between maximization of the long-term fitness and minimization of the variance, bet-hedging theory has recourse to two arguments: (a) the geometric mean fitness , 9 and .The first approach puts forward the principle that the long-term fitness is the geometric mean of the growth rate per generation . Maximizi and i, respectively, and n is the total population size , is related but not equivalent argument specifically based on the two-allele model of population dynamics. Gillespie showed that which of two alleles has a better chance of fixation is determined by the size of ion size . Thus, lγ ≃ μexp ( − σ2/(2μ2)). For the growth rate (fitness) l, a random variable, μ = E(l) is the arithmetic mean, σ2 = Var(l) is the variance, γ = exp (E(log l)) is the geometric mean. Thus, the geometric mean γ may be increased by decreasing the variance σ2 even though the arithmetic mean μ is reduced. It should be noted that these results are derived based on mathematical and biological assumptions. The symbol ≃ indicates a mathematical approximation of retaining up to the second-order term in the series expansion of log l around the mean value μ of l  and (b) are consistently related as we see from the approximate relation , is evaluated by means of the cumulative distribution function (cdf) of the standard normal distribution (with mean 0 and variance1),x) is a monotonically increasing function of the argument x, minimizing the probability Prob(tS ≤ K) is equivalent to minimizing the argumentt ≫ log (K/S0)/μllog , the ratio K/S0 being a fixed constant.We consider that population/asset size llog , i.e. whether tl. In fact, suppose that the random variable tl takes one of discrete values l1, l2, ..., with probabilities P1, P2, ..., respectively. The probabilities iP  add up to unity: ∑iiP = 1. The mean μllog  and variance tl are given byil. Consequently, to maximize the geometric mean is to maximize μllog . We stress that to maximize the long-term growth rate llog /σllog  in Eq.llog  in Eq. The present result is consistent with the previous result ,14 that wth rate . In a digiven by\documentgiven by\documentHere ,we consider two examples of risk hedging in biology and optimal hedging in repeated betting.Pieris rapae) .The first example is the risk-spreading behavior of the cabbage butterfly (s rapae) ,17. We cf be the ratio of the eggs deposited in variable (unpredictable) habitat 1. The rest 1 − f are deposited in constant (predictable) habitat 2. A fractional value of f, 0 < f < 1, signifies a risk-spreading behavior.Let iiliP should be maximized, the result being optimal at f = 1, i.e. no risk-spreading. In the long term, however, a different result is obtained by maximizing μllog  or μllog /σllog . The growth rate is a random variable taking values l1 = fr11 + (1 − f)r2 and l2 = fr12 + (1 − f)r2 with probabilities P1 = 1/3 and P2 = 2/3, respectively. Thus, μllog  and σllog  are evaluated from Eqs. In the short term, the ordinary mean, or the expected value, μ = ∑llog /σllog  as well as the geometric mean μllog  as a function of f. We find that both criteria, μllog /σllog  and μllog , similarly predict optimality of the risk-spreading behavior, i.e. an optimal value f* between 0 and 1. Quantitatively, however, the two criteria give different values for f*. As indicated with arrows in Fig. llog /σllog  is maximized at f* = 0.41, while μllog  is at f* = 0.62. The difference originates from the f-dependence of the variance σllog .In Fig. llog , σllog , log l1, and log l2 against f. Since μllog  is the probability-weighted average of log l1 and log l2, all these three quantities converge toward a common value at f = 0, where we obtain l1 = l2 = r2 with certainty. Thus, σllog  increases steadily from 0 to a maximum value as f increases from 0 to 1 , but he loses the wager (r2 = 0) with a 40% chance. The gambler repeats betting a fraction f of his current wealth while keeping the rest fraction 1 − f intact. The problem is to find optimal value of f. The Kelly criterion determines the optimal value f* such that the geometric mean μllog  is maximized. Substituting l1 = fr2 + 1 − f with P2 = 0.4 and l2 = fr1 + 1 − f with P1 = 0.6, we obtain μllog /σllog  and μllog , as shown in Fig. llog /σllog  and μllog  are different. The present criterion μllog /σllog  gives f* = 0, namely no betting at all. This result is qualitatively different from what is concluded from the Kelly criterion, according to which it is optimal to bet 20% of his bankroll (f* = 0.2). As in Fig. llog  , the peak height is too shallow to withstand a strong pressure toward f = 0 to minimize the denominator σllog  in μllog /σllog . In fact, this is a special case. Owing to l1 = l2 = 1 at f = 0, μllog /σllog  becomes almost independent of f. Therefore, diversification is not favored in this case (llog ), but irrational according to the present measure (μllog /σllog ).As a second example, we consider the optimal size for a bet, known as the Kelly criterion ,15. In ahis case . This istl, while we considered the simplest case of discrete choice in the last section. In this section, we consider another special case of the normal distribution in order to discuss the trade-off relation in general terms.The main results in the penultimate section apply to any probability distribution of il is normally distributed around μ, namely il = μ + δi, μllog  in Eq. 2 even if the ordinary mean μ is reduced  over the short-term success (of increasing the mean μ) (2 suggests that the derivative dμ/d(σ2) serves as the strength of the trade-off, in terms of which the condition for bet-hedging may be stated theoretically. The condition for the chances of survival to increase as σ2 decreases is given by ∂(μllog /σllog )/∂(σ2) < 0, from which we obtaindμ/d(σ2)) should be weaker than a threshold value on the right-hand side.In investment decisions, the trade-off is resolved by the individual investor. In evolutionary ecology, the trade-off is resolved by natural selection. In fact, many organisms appear to prioritize the long-term survival (by reducing the variance σ mean μ) ,19. The μ and variance σ2, we may apply the above result to the two examples in the last section. In panels Fig. dμ/d(σ2) is plotted with a solid curve, along with the right-hand side of Eq. f > 0.52 .Assuming that the statistical properties of interest are satisfactorily represented with the mean .52 Fig.  and f > .72 Fig. . Thus, wllog  in Eq. llog  is shown to be a concave function of f, whereas the concavity alone does not rationalize bet-hedging (0 < f < 1). Bet-hedging is concluded only if μllog  is unimodal  has been considered as an extinction measure in recent studies on the persistence of species populations with spatial heterogeneity   , and ariss (AFT)  correspof or not ,12,28,29tl diminishes rapidly as population size increases. In short, stochasticity has no sizable effect on within-generation bet-hedging. If we view this nullification of stochastic uncertainty as a strategic adaptation, it is most adequately explained as a result of the decrease in the variance σllog , without any resort to the mean μllog . In fact, there is no point in having recourse to the latter, or geometric mean, as it is no different from arithmetic mean μ in this case.In closing, we point out a novel insight gained from the present result. It is known that bet-hedging is not favored in fine-grained environments , 30. IndIn conclusion, we presented a new principle of diversification strategies, or bet-hedging, which is based on the maximization of the survival probability in the long term. The principle is to maximize the ratio of mean and standard deviation of the logarithmic growth rate. As compared with a conventional method by geometric mean, the present criterion gives a qualitatively similar but quantatively distinct result on the optimal fraction for diversification. Lastly, we should note that empirical data on bet-hedging strategies are not comprehensive enough to distinguish which method of bet-hedging theory is better than another , 19, whiThe authors thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.pgac228_Supplemental_FilesClick here for additional data file.


The turnover of microbial communities across space is dictated by local and regional factors. Locally, selection shapes community assembly through biological interactions between organisms and the environment, while regional factors influence microbial dispersion patterns. Methods used to disentangle the effects of local and regional factors typically do not aim to identify ecological processes underlying the turnover. In this paper, we identified and quantified these processes for three operational microbial subcommunities  from a tropical cascade of freshwater reservoirs with decreasing productivity, over two markedly different dry and rainy seasons. We hypothesized that during the dry season communities would mainly be controlled by selection shaped by the higher environmental heterogeneity that results from low hydrological flow and connectivity between reservoirs. We expected highly similar communities shaped by dispersal and a more homogenized environment during the rainy season, enhanced by increased flow rates. Even if metacommunities were largely controlled by regional events in both periods, the selection had more influence on free-living communities during the dry period, possibly related to elevated dissolved organic carbon concentration, while drift as a purely stochastic factor, had more influence on cyanobacterial communities. Each subcommunity had distinct patterns of turnover along the cascade related to diversity (Cyanobacteria), lifestyle and size (Free-living), and spatial dynamics (particle-attached). Spatial turnover is the variation in the species composition of communities across space and is controlled by an array of processes. In an analogy to the “big four” main processes governing population genetics, Selection dictates the composition of communities at the local scale and comprises constraints and pressures from both environmental conditions and biotic interactions such as competition and facilitation . Drift . Lastly,Intense mobility of organisms between local communities, or homogenizing dispersion (HD), would result in communities that are more similar to each other, whereas constrained movement results in less similar communities constrained by dispersal limitation DL; . HoweverIt can be difficult to disentangle the effects of regional (dispersion) and local (selection) factors in controlling the spatial turnover of microorganisms . PreviouIn general, environmental variables related to selection are correlated to spatial distance, making it challenging to partition the respective role of each component. Furthermore, the most common methods used to quantify the respective roles of environmental and spatial variables shaping community turnover do not discriminate between the different ecological processes underlying it .As a further complication, an accurate description of microbial community composition and diversity was for a long time not feasible . Recent phylogenetic turnover across habitats to define the composition and address the ecological processes underlying the observed community turnover. It assumes that lineages that are phylogenetically closely related also share traits and, consequently occupy more similar niches than distant relatives, instead of merely describing communities based on the distribution and richness of fixed cutoff OTU data , while regional factors relate to the spatial distance between reservoirs and residence time. We sampled the reservoirs during both dry and rainy periods, which directly influenced the volume of water flowing through the reservoirs, the inter-system connectivity, and, hence, the environmental homogeneity/heterogeneity. We hypothesized that communities seen during the dry period would mainly be influenced by local factors, i.e., selection in response to variation in environmental factors, while communities would mostly respond to regional processes during the rainy period due to higher system connectivity and mass effects. To achieve a better resolution of the processes driving spatial turnover, we divided the bacterial community into three operational subcommunities: Cyanobacteria, Particle-attached, and Free-living bacteria expected to respond to different local environmental drivers.The four reservoirs are part of the medium-low Tietê river system, Parana River basin, São Paulo state, Brazil . The rivSampling campaigns were conducted during the dry season in May (D1), July (D2), and September (D3) of 2015, and during the rainy season in November of 2015 (R1) and January (R2) and March (R3) of 2016. Using a GPS device, we revisited specific sampling points (deepest point) of each reservoir. One liter of water from the surface of each reservoir was processed by sequential filtration, on a 3 μm polycarbonate membrane and on a 0.22 μm cellulose acetate ester membrane filter (Sterivex). Membranes were immediately frozen in liquid nitrogen after filtration and transported to the laboratory for long-term storage at-80°C. Subcommunities studied were divided into heterotrophic bacteria and Cyanobacteria. Heterotrophic bacteria were represented by Particle-attached bacteria (PA) retained on 3 μm filters and free-living bacteria (FL), retained on 0.22 μm filters. Cyanobacterial community was represented by cyanobacteria retained on 3 μm filters.Environmental variables such as pH, temperature, and dissolved oxygen were measured with a multiparameter sonde YSI 6600 V2 . Chlorophyll-a was extracted following the methods of Dissolved organic carbon and total nitrogen were quantified using a Shimadzu TOC-V cph analyzer, equipped with a Total Nitrogen analyzer module following the manufacturer protocol. Free phosphate and sulfate were analyzed by ion chromatography using a Dionex ICS – 1,100 system (Thermo Scientific). Water transparency was measured using a Secchi disc. All environmental variables are summarized in We performed a principal component analysis (PCA) with standardized environmental parameters to explore the spatial structure of environmental conditions in both rainfall periods.DNA was extracted using the PowerSoil DNA Isolation kit (MoBio). Following extraction, the V3–V4 region of the 16S rRNA gene was amplified using primers 341F (5’-CCTACGGGNGGCWGCAG-3′) and 805R (5’-GACT ACHVGGGTATCTAATCC-3′) . High-thWe assessed the phylogenetic turnover of communities (evolutionary distance between OTUs found in two communities being compared) through the quantitative framework proposed by bray) to account for OTU relative abundance. At this second step, the deviation from the null model ranges from 1 to 1 and corresponds to homogenizing dispersion (RCbray < −0.95), limiting dispersal (RCbray > 0.95), or drift acting alone (|RCbray| < 0.95). For both randomization steps, the null model analysis returned a matrix of pairwise values between all four reservoirs.Thus, in the first step, the method considers the deviation of the original community from the null-model (beta-nearest taxon index - βNTI), returning communities governed by selection (S), |βNTI| > 2. Communities that did not fit this first group under selection (|βNTI| < 2) went through a second randomization step using the Raup-Crick  index moWe considered the ecological processes between directly connected reservoirs  or regional processes  or drift had the most influence on the composition of each of the three subcommunities.PERMANOVA analyses with communities’ “βMNTD weighted distance” were performed to determine if the composition of communities was statistically dissimilar. Given the prominent separation of sampling points by season in the PCA of the environmental variables , we firsThe dissimilarities between seasons and between reservoirs for all three subcommunities are illustrated in the NMDS –C, whereWith regard to the analysis of the underlying ecological process causing this species turnover between reservoirs free-living and particle-associated bacterial communities were mostly under influence of regional processes during both rainfall regimes . For CyaThe turnover of particle-attached communities was dominated by regional factors and slightly influenced by drift and local factors in both the dry and wet seasons . PERMANOThe overall ambition of the study was to identify the dominant ecological processes driving the turnover of microbial communities along a cascade of freshwater reservoirs with gradually changing water quality. We expected that the lower hydrological connectivity of the reservoirs during the dry period would result in stronger gradients in water quality, which would invoke higher selection pressure on microbial communities. Conversely, during the rainy season, the elevated hydrological flow would cause environmental homogenization and increase microbial dispersion, shifting the processes controlling bacterial community composition to predominantly regional processes.Microcystis, Planktothrix, and Cylindrospermopsis; Supplementary information 3). Our observations and results from previous studies point toward limited cyanobacterial diversity, likely caused by persistent and strong selection pressures within the system that favor these specific genera. Once such populations are established, selection pressure would weaken while drift would become increasingly important in such low-diversity communities  and temporally over seasons . Those dCompared with particle-attached bacteria, the free-living community subset are usually smaller  and moreSince variables linked to the trophic state of the ecosystem had a major impact on the environmental features , we hypoAs these systems are highly eutrophic and display a trophic state gradient, there will be decreasing primary production along the cascade . TherefoBesides the hypothesized role of DOC for subcommunities turnover, other environmental variables, such as pH and temperature, are known to influence bacterial community composition . HoweverAs a note of caution, the analytical framework proposed by The predominance of different ecological processes governing each subcommunity could be linked to features such as diversity, as cyanobacteria were less rich than the other subcommunities, and diversity limitation can favor drift. Free-living communities seemed to respond more directly to environmental conditions and selection while communities attached to particles are more susceptible to differences in characteristics of the particles with biogeography shaped by spatial dynamics. Dispersion rates, generation times, and potential of colonization are also important traits determining turnover patterns and vary across bacterial taxa, which should be subject to scrutiny in future studies.https://www.ncbi.nlm.nih.gov/, project PRJNA411849, sequences SRX5417418 to SRX5417491.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: HV, IB, and AV contributed to design and conception of this study. HV, IB, GM, RF, HS, and AV contributed to material preparation, samplings campaigns and data collection. HV, IB, and SB contributed to data analysis. All authors contributed to writing of the paper and approved this final version.Fundação de Amparo à Pesquisa (FAPESP) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) funded the project of which this study was part (processes 2011/50054–4 and 2014/14139–3). Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Programa de Pós-Graduação em Ecologia e Recursos Naturais (PPGERN) funded Helena Henriques Vieira, Guilherme Pavan de Moraes, and Roberta Mafra de Freitas. Grant Agency of Czech Republic (20-12496X) is funding Helena Henriques Vieira and open access fee.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Polymethyl methacrylate, or “bone cement,” can be used intraoperatively to replace damaged or diseased bone and to deliver local antibiotics. 3D printed molds allow surgeons to form personalized and custom shapes with bone cement. One factor hindering the clinical utility of anatomically accurate 3D printed molds is that cured bone cement can be difficult to remove due to the strong adhesion between the mold and the bone cement. One way to reduce the adhesion between the 3D printed mold and the cured bone cement is with the use of a surface coating, such as a lubricant. This study sought to determine the optimal surface coating to prevent bone cement adhesion to 3D printed molds that could be utilized within a sterile operating room environment.Hemispheric molds were 3D printed using a stereolithography printer. The molds were coated with four sterile surface coatings available in most operating theatres . Polymethyl methacrylate with tobramycin antibiotic was mixed and poured into the molds. The amount of force needed to “push out” the cured bone cement from the molds was measured to determine the efficacy of each surface coating. Tukey’s multiple comparison test was performed to compare the results of the pushout test.p = 0.0123).The average pushout force for the surface coatings, in increasing order, were as follows (mean ± standard deviation) --- bacitracin ointment: 9.10 ± 6.68 N, mineral oil: 104.93 ± 69.92 N, lubricating jelly: 147.76 ± 63.77 N, control group: 339.31 ± 305.20 N, ultrasound transmission gel 474.11 ± 94.77 N. Only the bacitracin ointment required significantly less pushout force than the control (The bacitracin ointment was the most effective surface coating, allowing the bone cement to be pushed out of the mold using the least amount of force. In addition, the low standard deviation speaks to the reliability of the bacitracin ointment to reduce mold adhesion compared to the other surface coatings. Given its efficacy as well as its ubiquitous presence in the hospital operating room setting, bacitracin ointment is an excellent choice to prevent adhesion between bone cement and 3D printed molds intraoperatively. In aOne challenge to overcome with PoC 3D printed molds  for bone cement, however, is that bone cement can become strongly adherent to the surface of the mold during the curing process of the bone cement , 16. Thi). The molds were designed with a 1 cm diameter hole at the bottom to allow for a probe connected to a force sensor to push out the bone cement during experimentation. The molds were printed using a Form 3BL  stereolithography printer using the biocompatible Biomed Amber resin with 0.100 mm z-axis resolution . In addition to the molds, plugs for the holes at the base of the molds were designed and printed  , bacitracin zinc ointment , MediChoice lubricating jelly , and ultrasound transmission gel . The mineral oil was in liquid form, and the three other surface coatings were in gel form. The bacitracin ointment has a petroleum jelly base, and the lubricating jelly and ultrasound gel were water-based. Thus, there were two oil-based and two water-based surface coatings tested. To apply the mineral oil, the molds were submerged in the liquid for 5 min. To apply the gels, the molds were coated with a thin layer with a gloved hand. The exact amount gel applied varied slightly between cups, but could be described as “the minimum amount required to coat the entire surface.” This approach was chosen to reflect what could be done easily in the operative setting. After a given mold was lubricated, the plug was inserted into the base of the mold.n = 6), bacitracin ointment (n = 6), mineral oil (n = 7), lubricating jelly (n = 6), ultrasound transmission gel (n = 3).Simplex P with Tobramycin antibiotic bone cement  was chosen due to its low viscosity, allowing it to be poured, which ensured an even distribution within the mold. The bone cement was mixed per the instructions and poured into each mold. Only enough cement to pour into four molds was prepared at a time so that cement was poured into each mold at roughly the same time in the cement cure process (within 30 seconds of each other) for intra and inter batch consistency. If a mold was not filled within this 30-second window, it was discarded. A filled mold can be seen in Fig. While the bone cement was curing, the plugs were rotated every minute to ensure that the cement did not adhere to the plugs. This allowed the plugs to be removed once the cement hardened. The bone cement was allowed to cure for 20 minutes, which was sufficient time for it to fully cure at room temperature (22C), per the bone cement manufacturer’s protocol [In order to determine the efficacy of the different lubricants, the force required to push out the bone cement from each mold was measured. A custom 3D printed jig was used to hold the molds so the cement could be pushed out Fig. . Molds wn = 6), mineral oil: 104.93 ± 69.92 N (n = 7), lubricating jelly: 147.76 ± 63.77 N (n = 6), control group: 339.31 ± 305.20 (n = 6), ultrasound transmission gel N 474.11 ± 94.77 N (n = 3) . Only the bacitracin ointment required significantly less pushout force than the control (p = 0.0124). The individual data points can be found in Table The average pushout force for the surface coatings, in increasing order, were as follows (mean ± standard deviation (sample size)) --- bacitracin ointment: 9.10 ± 6.68 N (p = 0.0124). Not only did the bacitracin group have the lowest average pushout force, it also had the lowest standard deviation of any group, an indication of its reliability as a surface coating. Though not included in the data due to inconsistent experimental protocols, it should be noted that during pilot studies the bacitracin ointment was sometimes so effective the bone cement would sometimes simply fall out of the mold before testing. While the other surface coatings had lower average pushout forces than the controls, their large standard deviations make them inconsistent choices for delicately casted parts.The goal of this study was to identify which surface coating available in the surgical setting would be most effective at preventing adhesion between bone cement and a 3D printed mold. To accomplish this, the force required to remove bone cement from identical molds with various surface coating was measured and compared. The bacitracin ointment was the most effective and reliable lubricant for preventing bone cement-mold adhesion and the only lubricant with a pushout force significantly lower than the control group  between mixing the cement and adding it to the mold lead to significantly lower forces needed to push the hardened cement out when using the high viscosity cement. This relationship seemed to apply to the low viscosity cement as well, though on a longer timescale (> 2 minutes). In summary, low viscosity bone cement appears to be optimal for casting compared to high viscosity bone cement.This study has a few limitations. First, this study examined the efficacy of surface coatings on 3D printed molds constructed using a single type of resin with a single set of printer settings and printed with a single orientation. Thus, though one surface coating clearly performed better than the others in this experiment, further work needs to look at the generalizability of the results. In particular, the use of surface coatings to prevent adhesion between bone cement and molds made from other types of resins, especially those with other physical properties, requires more exploration. A second limitation of this study is the relatively small sample sizes. Thus, while large differences in surface coating performance could be found, larger sample sizes are needed to find subtler performance differences. Third, this study did not look at other methods of preventing bone cement adhesion such as making physical alterations to the mold surface itself (e.g. sanding the mold).Of the surface coatings tested, including a range of oil-based and water-soluble options, we found that bacitracin ointment was the most reliable and effective. Given bacitracin ointment’s efficacy and ubiquity in the hospital operating theatre, we recommend bacitracin ointment as a preferred surface coating. Prior to using bacitracin ointment, surgeons should confirm that the patient does not have a bacitracin allergy. Further investigation should explore factors which affect mold performance such as mold geometry. Furthermore, variables related to stereolithography printers should be explored, such as different biocompatible resins and adjustable printer settings such as print orientation and z-axis resolution. Ultimately, the authors hope that this work will aid physicians working to improve patient care through personalized medicine.