CHEMPHYSCHEM MINIREVIEWS DOI: 10.1002/cphc.201301230

Designing Fractal Nanostructured Biointerfaces for Biomedical Applications Pengchao Zhang[a, b] and Shutao Wang*[a] Fractal structures in nature offer a unique “fractal contact mode” that guarantees the efficient working of an organism with an optimized style. Fractal nanostructured biointerfaces have shown great potential for the ultrasensitive detection of disease-relevant biomarkers from small biomolecules on the nanoscale to cancer cells on the microscale. This review will

present the advantages of fractal nanostructures, the basic concept of designing fractal nanostructured biointerfaces, and their biomedical applications for the ultrasensitive detection of various disease-relevant biomarkers, such microRNA, cancer antigen 125, and breast cancer cells, from unpurified cell lysates and the blood of patients.

1. Introduction The development of biointerfaces has been promoted by the demand for the precise detection of disease-relevant biomarkers, which have great potential application in the diagnosis and monitoring of various diseases. Traditional two-dimensional (2D) biointerface-based methods, such as fluorescence immunoassays[1] and enzyme-linked immunosorbent assays (ELISA),[2] can satisfy the routine detection of biomarkers. However, the performance of those methods is not satisfactory in detecting the extremely low abundance of biomarkers in clinical samples. For example, microRNAs (miRNAs)[3] are involved in tumor metastasis,[4] differentiation, renewal of stem cells,[5] and replication of virals.[6] Cancer antigen 125 (CA-125)[7] is known to be correlated to ovarian cancer. Circulating tumor cells (CTCs) in peripheral blood of patients can be regarded as a “liquid biopsy” of tumors.[8] Recently, fractal structures have attracted intensive interest due to their special properties. Different from commonly used nanostructures[9] such as nanoparticles, nanowires, nanotubes, and nanofibers, which have been used to improve the performance of biosensors, fractal structures can provide a unique “fractal contact mode” to increase the number of valid contact sites, which improves the diffusion efficiency; this makes biomarkers easily accessible and, consequently, enhances the detection sensitivity. Fractal nanostructured biointerfaces have shown great potential for the ultrasensitive detection of disease-relevant biomarkers from small biomolecules on the nanoscale to cancer cells on the microscale.

[a] Dr. P. Zhang, Prof. S. Wang Beijing National Laboratory for Molecular Sciences (BNLMS) Key Laboratory of Organic Solids, Institute of Chemistry Chinese Academy of Sciences (ICCAS), Beijing, 100190 (P.R. China) Fax: (+ 86) 010-82627566 E-mail: [email protected] [b] Dr. P. Zhang University of Chinese Academy of Sciences Beijing 100049 (P.R. China)

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This review will present the basic concepts and advantages of fractal nanostructures, several recent developments in designing fractal nanostructured biointerfaces, and their biomedical applications. First, we will introduce fractal structures existing in nature and their special properties and the design of fractal nanostructured biointerfaces. Then, we will introduce methods for the preparation of fractal nanostructured biointerfaces. Furthermore, we will summarize recent developments in the use of fractal nanostructured biointerfaces for the detection of nucleic acids, proteins, cells, and other disease-relevant biomarkers. Finally, current and further challenges of fractal nanostructured biointerfaces are addressed and prospected. It should be noted that fractal nanostructures at the molecular level, such as dendrimers[10] and multivalent ligands,[11] are not included in this review, as previous reviews[12] have summarized the applications of dendrimers in biosensing.

2. Design of Fractal Nanostructured Biointerfaces Fractal structures are primarily used to describe the various natural structures that can be split into parts, each of which is self-similar.[13] Fractal structures in organisms can provide a unique “fractal contact mode” that guarantees the efficient working of organisms with an optimized style.[14] For example, bronchial trees[15] maintain the rapid exchange of oxygen and carbon dioxide at the lung surface. Vasculature[16] and neurons[17] are able to efficiently transport nutrition and transmit signals, respectively. As a prototype of a natural biosensor, the nasal membrane[18] can sense smell ultrasensitively due to the large number of fractal structures on its surface, which can provide increased contact sites for analytes. As shown in Figure 1 a, fractal nanostructures offer a kind of “fractal contact mode”, which ensures an outward, three-dimensional (3D), and spatial contact mode for various diseaserelevant biomarkers. Fractal nanostructures can supply a huge surface area, provide suitable orientation and increased effiChemPhysChem 0000, 00, 1 – 13

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Figure 1. The different contact modes provided by a) outward and b) inward fractal nanostructures. The outward fractal nanostructures can facilitate the contact of biomarkers more easily than the inward fractal nanostructures.

cient contact sites, make biomarkers easily accessible to probes and, thus, promote higher efficiency and sensitivity. Furthermore, fractal nanostructures can enhance the topographic interaction with cells by matching the surface structures. However, it is difficult for biomarkers to access the surface of inward fractal nanostructures (Figure 1 b). Until now, artificial fractal nanostructures have been utilized in novel antennas,[19] solar cells,[20] and ultrasensitive biosensors.[21]

Shutao Wang received his B.S. (2000) and M.S. (2003) degrees from Northeast Normal University and his Ph.D. degree (2007) from the Institute of Chemistry, Chinese Academy of Sciences (ICCAS) under the supervision of Professor Lei Jiang. He then worked in the Department of Molecular & Medical Pharmacology and the California NanoSystem Institute at the University of California at Los Angeles as a postdoctoral researcher (2007–2010). He was appointed as a full Professor of Chemistry in 2010 at ICCAS. His research interests include the design and synthesis of bioinspired interfacial materials with special adhesion and their applications at the nano-biointerface. Pengchao Zhang is currently a Ph.D. student at ICCAS. He received his B.S. degree (2010) in materials science from Wuhan University of Technology, China. In 2010, he joined ICCAS under the supervision of Professor Lei Jiang and Professor Shutao Wang. His current scientific interests are focused on pursuing the bioinspired special adhesion of nanosurfaces towards manipulating rare cancer cells and the early diagnosis of cancers.

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www.chemphyschem.org Fractal nanostructured biointerfaces can achieve ultrasensitive detection of nucleic acids and proteins.[22] To systematically study the relationship between nanostructures and detection sensitivity, Kelley and co-workers[23] generated three kinds of nanostructured microelectrodes (NMEs) with different roughness [defined as smooth, moderate, and fine nanostructures (i.e. fractal nanostructures)]. As shown in Figure 2 a, hybridization kinetics indicate that the fractal NME exhibits a much faster response than the moderate NME. In addition, the surface coverage of fractal NME is higher than that of moderate or smooth NME.[24] Correspondingly, the fractal NME exhibits the highest hybridization efficiency; moderately nanostructured NMEs show lower hybridization efficiency, and smooth NMEs show the lowest hybridization efficiency (Figure 2 b). As a result, the detection limits are 0.010, 10, and 100 fm for fractal NME, moderate NME, and smooth NME, respectively.[23] The authors proposed a model to explain their results as well (Figure 2 c). By studying how the radius of the curvature of the nanoparticles affects the surface density of DNA, Mirkin and co-workers demonstrated that a smaller radius of curvature provided a larger deflection angle between the probes, which thus promoted a higher surface coverage.[25] Accordingly, the authors predicted that the fractal NME had larger defection angles, which might be responsible for the enhanced hybridization efficiency, whereas the smooth NME had a small defection angle, which was related to the low hybridization efficiency.[24] Hence, the physical mechanism of the enhanced sensitivity of fractal nanostructures can be attributed to three aspects: 1) fractal nanostructures can supply a huge surface area, which increases the amount and density of probes; 2) a unique 3D contact mode can provide suitable orientation and increased efficient contact sites between biomarkers and probes; 3) fractal nanostructures may diminish repulsion as the target approached the immobilized probe, and this would enhance the hybridization efficiency. Furthermore, fractal nanostructures found on the surface of cancer cells result in a higher fractal dimension (Df) of cancer cells than that of normal cells, which can be used to discriminate cancer cells from normal cells.[26] Inspired by this discovery, we programmed three kinds of fractal gold (Au) nanostructures (FAuNSs) with different values of Df (defined as low FAuNSs, moderate FAuNSs, and high FAuNSs) and developed a new kind of biointerface on the basis of these FAuNSs for the highly efficient capture of circulating breast tumor cells (i.e., MCF7 cells) from whole-blood samples.[27] After the FAuNS interfaces were coated with the epithelial cell adhesion molecule antibody (anti-EpCAM), the detection sensitivity was improved as much as 21 times for an EpCAM-positive cell line relative to that of flat Au interfaces. Detailed experiments revealed that the topographic interaction between the tumor cells and FAuNSs was vastly different (Figure 3) by exhibiting different numbers of filopodia. More filopodia (33–54 per cell) were stretched out by the cells on the moderate FAuNS interfaces (Figure 3 b) than those (27–39 per cell) on the low FAuNS interfaces (Figure 3 a). The cells on the high FAuNS interfaces had even more filopodia (44–82 per cell, Figure 3 c). In addition to biomolecular recognition, the similar values of Df between ChemPhysChem 0000, 00, 1 – 13

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Figure 2. a) Hybridization kinetics of fractal and moderate NMEs. Reprinted by permission from ref. [23]. Copyright 2009 Macmillan Publishers Ltd. b) Surface coverage (black bars) and hybridization efficiency (gray bars) of three different Pd NMEs. Reprinted with permission from ref. [24]. Copyright 2010 American Chemical Society. c) Proposed physical mechanism for the enhanced hybridization efficiency of fractal NMEs. Reprinted with permission from ref. [24]. Copyright 2010 American Chemical Society. ssDNA = single-stranded DNA.

the high FAuNSs and the cancer cells induced topographic recognition, which resulted in a dramatic enhancement in the capture efficiency of the cancer cells. Therefore, by matching the surface structures, fractal nanostructures enhanced the detection limits for large biomarkers, including viruses, pathogens, bacteria, and CTCs.

3. Fabrication Methods for Fractal Nanostructures Up to now, numerous efforts have been made to synthesize fractal nanostructures, and several different methods have been developed (as listed in Table 1). Among the various fabrication methods, electrochemical deposition and galvanic displacement are the most promising approaches owing to their versatility for the generation of precisely defined structures.  2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

3.1. Electrochemical Deposition Electrochemical deposition[45] remains the most common, simplest, and most extensively used method to create fractal nanostructures regardless of the size and shape of the substrate. Metals (including noble and transition metals), metal oxides, and semiconductors can be fabricated by using this method. For a typical synthesis of fractal nanostructures, electrochemical deposition is performed in a conventional threeelectrode cell. Usually, Ag/AgCl acts as the reference electrode, Pt plate/wire acts as the counter electrode, and a conductive wafer acts as the working electrode. Several factors such as electrodepositing potential,[46] reagent concentration,[47] supporting electrolytes,[23] deposition time,[48] and additives[49] can affect the morphology of the prepared nanostructures. Therefore, by properly regulating these parameters, fractal nanostructures can be obtained. ChemPhysChem 0000, 00, 1 – 13

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Figure 3. Typical environmental SEM images of captured MCF7 cells on a) low FAuNS interfaces, b) moderate FAuNS interfaces, and c) high FAuNS interfaces. Middle and bottom images are fractal Au nanostructures. Reprinted with permission from ref. [27]. Copyright 2013 Wiley-VCH Verlag GmbH & Co. KGaA.

Table 1. Methods for the fabrication of fractal nanostructures. Method

Material

Ref.

electrochemical deposition

Zn Cu Au Ag Au/Pt CuNi Pd Ag Au Cu Pt Pd Ni Co Zn Au Pd Pt Ag cystine silica Ag Ag LaFeO3 Si

[28] [29] [30] [31] [32] [33] [23] [34] [35] [36] [36] [36] [36] [36] [36] [37] [37] [37] [38] [39] [40] [41] [42] [43] [44]

galvanic displacement reaction

surfactant-assisted reduction

acoustic cavitation template photocatalytic synthesis photochemical growth hydrothermal deep reactive ion etching

The electrodepositing potential has a great influence on the formation of fractal nanostructure because it is relevant to the kinetics of electrodeposition.[29, 50] Increasing the depositing potential accelerates the kinetics of electrodeposition, which pro 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

motes the irregular growth of nanostructures. A smooth sphere is generated at a low deposition potential, and fractal Au nanostructures can be obtained if a higher deposition potential is applied.[27] At a certain electrodepositing potential, varying the growth time can tune the morphology, size, and density of the nanostructures. For example, Zhang and co-workers studied the time-dependent growth of fractal silver (Ag) nanostructures.[31] At the initial time of electrodeposition (2 s), there were a few discrete Ag aggregates. Increasing the deposition time (200 s) resulted in denser and larger Ag nanostructures. A further increase in the deposition time to 1600 s resulted in the formation of a high coverage of fractal Ag nanostructures. In addition, the supporting electrolyte can also significantly affect the morphologies of the deposited nanostructures by controlling the ionization of the metal ions. Chen and co-workers demonstrated that by increasing the concentration of Ag ions from 1 to 20 mm, the morphology of the formed nanostructures changed from Ag aggregates to fractal Ag nanostructures with larger dimensions and higher coverage.[47] Fractal Ag nanostructures were obtained by increasing the concentration of KNO3 from 1 to 10 mm. However, a further increase in the concentration of KNO3 to 15 mm did not lead to any change in the morphology.[51] In general, ideal fractal nanostructures cannot be fabricated by regulating one parameter. Therefore, two or more parameters are always regulated together at the same time. By properly controlling the electrodepositing potential and the supporting electrolyte, programmable fractal nanostructures can be easily fabricated. For example, we recently fabricated three kinds of fractal Au nanostructures with different values of Df by changing the electroplating potential (from 0.30 to 0.10 V) and concentrations of chloride ions (Cl , from 0 to 1.8 m) in the supporting electrolyte.[27] At a low potential ( 0.10 V) and a high concentration of Cl (1.8 m), relatively smooth nanostructures were produced. Nanostructures with more complex details were generated at a higher potential ( 0.15 V) and a lower concentration of Cl (0.9 m). A further increase in the electroplating potential to 0.30 V in the absence of Cl in the electrolyte resulted in the production of highly fractal Au nanostructures. Similarly, programmable fractal palladium (Pd) nanostructures were generated by altering the electrodepositing potential and the supporting electrolyte.[23] Besides the above parameters, additives in the supporting electrolyte, such as polyvinylpyrrolidone,[49a] polyglycol,[49b] supramolecular complexes of dodecyltrimethylammonium bromide and b-cyclodextrin,[49c] pluronic F127 block copolymer,[49d] carbon nanotubes,[49e] and cetyltrimethylammonium bromide,[49f] can act as specific capping agents or structure-directing agents to influence the formation of fractal nanostructures. Generally, those additives may introduce some impurities that need further purification. Furthermore, self-assembled layers modified on the surfaces including polyamidoamine dendrimer monolayers[32, 52] and multilayers of poly(diallyldimethylammonium chloride) and poly(4-stylene sulfonate)[30, 31] can assist the nucleation and growth of fractal nanostructures and improve the stability of the formed fractal nanostructures. ChemPhysChem 0000, 00, 1 – 13

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CHEMPHYSCHEM MINIREVIEWS 3.2. Galvanic Displacement Reaction The galvanic displacement reaction,[53] the driving force of which comes from the difference in redox potentials of the two involved metals, generates nanostructures through sacrificing a metal for another metal if they come into contact with each other in a solution phase. In comparison with electrochemical deposition, the galvanic displacement reaction is an electroless deposition process and can be used as a simple, versatile, and robust method for the creation of novel nanostructures. Variations in the concentration of the metal ion,[54] reaction time,[55] additives,[35, 36] as well as solvents[56] can affect the morphology of the obtained nanostructures by controlling the nucleation and growth of the nanostructures. Generally, the morphology of the generated nanostructures changes along with the reaction time, and diverse nanostructures can be obtained by controlling the reaction time. Zhang and co-workers[34] showed that an Ag layer with little branches was deposited on a substrate within 2 min. By increasing the deposition time to 10 min, the size of the Ag nanostructures increased and the whole surface was covered with microscaled fractal Ag bearing nanoscale branches at a deposition time of 60 min. Increasing the concentration of AgNO3[34] or introducing sulfate ions[57] sped up the deposition. Similar fractal nanostructures were obtained in a shorter time. In addition, Wong and co-workers[56] demonstrated that fractal Ag nanostructures could be fabricated in water-based electrolytes, whereas Ag flowers, plates, and blocks were obtained by using ethylene glycol as the solvent. Assisted by NaF or NH4F, Li and co-workers[36] recently achieved a large-scale production of fractal Ag and Au as well as other metals (Cu, Pt, Pd, Ni, Co, and Zn) on commercial aluminum foil. Fluoride added into the immersion plating solution induced rapid dissolution of aluminum oxide, which thus resulted in a continuous galvanic replacement reaction. This facile, economical, and general approach offers a promising route for the large-scale production of metal nanomaterials for industrial applications.[58]

4. Advanced Application in Biomedical Applications In this section, we summarize recent developments in fractal nanostructured biointerfaces for biosensing and biomedical applications. A nanomolar to attomolar detection limit has been achieved for a variety of analytes (Table 2). 4.1. Nucleic Acids In principle, electrochemical detection offers the capability for sensitive electronic readout and specific analysis, which can sufficiently enable the direct detection of nucleic acids in solution—without resorting to enzymatic amplification approaches such as polymerase chain reaction (PCR) —with the aid of very simple, low-cost instrumentation. Kelley and co-workers reported that fractal Pd NMEs tethered by peptide nucleic acids allow rapid, ultrasensitive, label-free detection of complementary DNA and miRNA by using an electrocatalytic reporter  2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

www.chemphyschem.org Table 2. Fractal nanostructure based biosensors for the detection of various biomarkers. Material

Analyte

Sensitivity

Sensor type

Ref.

electrocatalysis electrocatalysis electrocatalysis electrocatalysis DPV DPV SWV multiplexed colorimetric QCM impedimetric sensor electrocatalysis electrocatalysis electrocatalysis

[59] [23] [46] [60] [61] [62] [63] [38]

10 cells

electrocatalysis

[68]

100 pg mL 1 1.5  10 14 m 5.7  10 15 m 1 pg mL 1 10 3 m 10 5 m 10 7 m 10 9 m 62  10 9 m 10 5 m

DPV DPV DPV DPV SERS amperometry amperometry cyclic voltammetry amperometry amperometry

[69] [70] [21] [71] [58b] [33] [49b] [72] [43] [54]

18

Pd Pd Pd Pd Au Au/GR[a] Au Ag

DNA DNA DNA miRNA DNA DNA DNA DNA

10 m 10 17 m 10 15 m 10 17 m 10 15 m 2.9  10 3 m 12  10 18 m 50  10 9 m

Au cystine Au/Pd Au Pd

DNA cDNA mRNA (E. coli) mRNA (E. coli) mRNA (cancer cell) mRNA (cancer cell) CA-125 thrombin thrombin CEA glucose glucose H2O2 benzo[a]pyrene dopa H2O2/glucose

10 12 m 10 14 m 1.5 cfu mL 1 cfu mL 1 1 ng mL 1

Au Au Au Au Au Ag CuNi Pd SiO2 LaFeO3 Ag

1

[64] [39] [65] [66] [67]

[a] GR = reduced graphene.

system.[23] An electrochemical current is generated by reducing positively charged Ru(NH3)63 + , which serves as a DNA-binding electron acceptor, by using a differential pulse voltammogram (DPV) technique. By introduction of Fe(CN)63 , which acts as an anionic electron acceptor, the electrical current is further amplified, and this facilitates high-sensitivity readout. The authors realized precise detection of fewer than 100 target DNA molecules (1 am) by using these fractal Pd NMEs.[59] Furthermore, the specific and sensitive detection of miRNA was realized on the same fractal Pd NMEs. The fractal Pd NMEs were able to distinguish not only two closely related sequences, miR-26a and miR26-b, but also a precursor miR-21 sequence (full length and double stranded) and a mature miR-21 sequence (shorter and single stranded). Importantly, RNA samples extracted from normal oral epithelial cells and human head and neck squamous cancer cells (FaDu, UTSCC-8, and UTSCC-42a) grown in culture were detected as well. The authors profiled two different microRNAs, miR-21 and miR-205, the abundances of which were judged to be more than 100-fold higher in the cancer cell lines than in normal epithelial cells, and selected RNU44 as a control RNA molecule. The levels of both miR-21 and miR205 were significantly increased in the cancer cell lines, whereas the level of RNU44 remained constant in all four cell lines.[60] Clinical samples, such as blood and tumor tissues, are much more complicated than artificial nucleic acid samples. To verify the performance of fractal Pd NMEs in clinical samples, Kelley and co-workers further directly detected messenger RNA (mRNA) samples for prostate cancer related gene fusions in ChemPhysChem 0000, 00, 1 – 13

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CHEMPHYSCHEM MINIREVIEWS cell extracts and tumor tissues from prostate cancer patients.[67] The fractal Pd NME accurately identified gene fusions associated with aggressive prostate cancer and distinguished them from fusions that correlated with slower-progressing prostate cancer. However, the detection limits were significantly degraded. As critical as the chemical reaction itself, the transport of target molecules to the sensors governs the binding kinetics[73] and ultimately the performance in surface-based biosensors. To improve the performance of biosensors in clinical samples, it is greatly needed to overcome the diffusion barriers in passive solutions.[74] Thus, Kelley and co-workers increased the length of the fractal NME from 10 to 100 mm (Figure 4) and achieved the rapid and direct detection of b-mRNA (rpoB) from solutions of unpurified bacterial lysate generated from cultured

Figure 4. Sensors with three different sizes (10, 30, and 100 mm) and their detecting capacity of mRNA solutions of either 1.5 or 150 cfu mL 1 E. coli. Reprinted with permission from ref. [65] Copyright 2011 American Chemical Society.

E. coli.[65, 66] The 100 mm NME realized successful detection with lysates generated from 1.5 cfu mL 1 E. coli, whereas the 10 and 30 mm sensors did not produce a detectable response with the same concentrations. The long length of the 100 mm sensor resulted in its extension into the solution, which allowed it to interact with the target molecules; this decreased the slow travel of the target molecules through the solution to the fixed sensors and thus increased the detection rate. By using the same platform, the authors also successfully detected mRNA derived from as few as ten K562 cells (chronic myeloid leukemia cells) in unpurified and unprocessed lysate and in the presence of a 5 000 000-fold excess of blood cells.[68] This fractal nanostructure based platform is label-free and does not require the sample to be processed in any way; thus, it shows great promise for clinical use. Similarly, electrochemical DNA sensors based on a fractal Au nanostructure modified electrode were reported by using methylene blue (MB) as an electrochemical hybridization label. Li and co-workers achieved a wide linear range from 1 fm to 1 nm and a low detection limit of 1 fm toward complementary  2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

www.chemphyschem.org target DNA.[61] Sun and co-workers detected specific Listeria monocytogenes hly single-stranded (ss) DNA sequences in a linear concentration range from 10 12 to10 6 m with a detection limit of 2.9  10 13 m.[62] Malhotra and co-workers developed a fractal cystine-based impedimetric sensor that exhibited linear response to a clinical DNA sample of E. coli in the concentration range from 10 6 to 10 14 m with a response time of 30 min.[39] On the basis of fractal Au nanostructures, a “sandwich-type” DNA biosensor was also fabricated for ultrasensitive DNA recognition by a using square-wave voltammetry (SWV) technique (Figure 5).[63] First, report-DNA (rDNA) was chemiabsorbed on Au nanoparticles (AuNPs). Then, double-stranded (ds) DNA functionalized AuNPs (dsDNA-AuNPs) were obtained by hybrid-

Figure 5. Fabrication process of a “sandwich-type” DNA sensor. Reprinted from ref. [63] with permission from Elsevier. HAGMNs = hierarchically aloelike gold micro/nanostructures.

izing rDNA with its partially complementary target-DNA (tDNA). The other part of tDNA could hybridize with the capture-DNA (cDNA), which was immobilized onto the surface of the fractal Au nanostructured electrode; thereby, the dsDNAAuNPs could be immobilized on the electrode surface. Finally, the positive MB ions as electrochemical probes were bound to the anionic phosphate of DNA. Assisted by the dsDNA-AuNP complexes, which enhanced the readout signal by the adsorption of a large number of MB molecules, the “sandwich-type” DNA biosensor showed a significantly enhanced detection limit of 12 am, a wide linear response ranging from 50 am to 1 pm, as well as high selectivity, stability, and reusability. Lee and co-workers developed another sandwich-structurebased detection platform by using fractal Ag nanostructures for multiplexed colorimetric detection of target DNA sequences. Three distinctive plasmonic nanoparticles (Figure 6 a; triangular Ag nanoprisms: blue, spherical Ag nanoparticles: yellow, and spherical Au nanoparticles: red), were conjugated with probe DNA sequences (A2, B2, and C2).[38] Three sequences associated with the Ebola virus (A), human immunodeficiency ChemPhysChem 0000, 00, 1 – 13

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Figure 6. a) Scheme for the detection of target DNA sequences (A, B, and C) by using a multiplexed colorimetric strategy. b) Color changes before hybridization, after hybridization, and a reference of the nanoparticle probes without target DNA sequences. Reproduced from ref. [38] with permission of The Royal Society of Chemistry.

virus (B), and hepatitis B virus surface-antigen (C) genes were selected as targets. The probe DNA sequences (A1, B1, and C1) tethered on the surfaces of the fractal Ag nanostructures selectively bound one half of the specific target sequences (A, B, and C), whereas the other half of the targets was bound to the probe DNA sequences on the nanoparticles, and this resulted in distinctive color changes depending on the combination of the three targets. For example, the solution containing only one target exhibited the sum of the colors of the other two nanoparticles (A: red + yellow = orange; B: red + blue = violet; C: yellow + blue = green), whereas the solution containing two of the targets displayed the color of the other nanoparticles (A + B: red; A + C: yellow; B + C: blue, Figure 6 b). The optical signatures made it simple to clearly distinguish the presence of the specific target sequences and their combinations in the mixtures without resorting to any complicated instruments. Quartz crystal microbalance (QCM) is a simple, label-free, cost-effective, high-resolution mass-sensing technique that has been utilized to detect diverse analytes, such as gaseous species, carbohydrates, nucleic acids, proteins, drugs, and cells.[75] By immobilizing the nanogold hollow balls with fractal surfaces onto the surface of the QCM electrode, Jiang and co-workers extended the detection limitation of target DNA to 10 12 m.[64] Although the detection limit needs to be further improved, this study presents an alternative strategy for the detection of biomarkers by using fractal nanostructures. 4.2. Proteins Compared with analyzing nucleic acids, the precise detection of proteins is more challenging because 1) proteins are very sensitive to the surrounding environment such as pH, temperature, and ionic strength and 2) high background of interfering  2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

proteins with high abundance in most clinical samples can immensely disturb the identification of specific proteins. Fractal nanostructures present a promising potential for the sensitive detection of proteins. Kelley and co-workers applied the same platform that was previously used for the analysis of nucleic acids to the immunosensing of the ovarian cancer biomarker CA-125.[69] As shown in Figure 7 a, the current generated by [Fe(CN6)]3 /4 was attenuated after binding of CA-125, because CA-125 blocked the interfacial electron-transfer reaction of [Fe(CN6)]3 /4 . The detection system exhibited excellent specificity, low limit of detection down to 0.1 U mL 1 (  500 fm or 100 pg mL 1) with analysis of CA-125 in human serum (Figure 7 b) and whole blood (Figure 7 c). Notably, human serum albumin (HSA), the concentration of which in humans is constant and high, was chosen as an internal standard to minimize false positive and false negative errors. To amplify the electrochemical signal, “sandwich-type” protein biosensors were developed on the fractal Au nanostructure modified electrodes. By using thionine-functionalized mesoporous silica nanospheres as signal tags, fractal Au nanostructure tethered aptamers as ligands exhibited a wide linear range from 0.03 pm to 0.018 mm and a low detection limit of 15 fm for thrombin.[70] The electrochemical signal was efficiently amplified due to the high pore volume and large surface area of the signal tags. In addition, graphene[76] has captured worldwide interest owing to its attractive properties, which include its high electrical conductivity. Therefore, by using graphene oxide/poly(o-phenylenediamine)/nanogold hybrid nanosheets as signal tags on fractal Au nanostructure modified electrodes, a new electrochemical immunosensing protocol was designed for the detection of carcinoembryonic antigen (CEA) over a wide range of 0.005–80 ng mL 1 and a low detection limit of 5.0 pg mL 1.[71] Although these methods are promChemPhysChem 0000, 00, 1 – 13

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www.chemphyschem.org response in the range from 10 15 to 10 10 m and a detection limit of 5.7 fm; it thus shows great potential for clinical diagnosis of disease-related biomarkers.

4.3. Cells

Figure 7. a) Scheme for the electrochemical detection of the CA-125 antigen. b) Detection of CA-125 with different concentrations in spiked serum samples with HSA as an internal standard. c) Detection of CA-125 in undiluted, unprocessed blood. Reprinted with permission from ref. [69]. Copyright 2011 American Chemical Society.

Metastasis[77] is the major cause of morbidity and mortality in cancer patients. Until now, the rate of cancers being diagnosed has steadily increased; however, the normalized numbers of cancer-related deaths have remained virtually unchanged.[78] There are tremendous and urgent demands to diagnose cancer at an early stage. CTCs[79] play a crucial role during the progression of metastasis and have been regarded as an emerging cancer-related biomarker due to their potential applications in the examination of cancer metastasis, prediction of patient prognosis, and monitoring of the therapeutic outcomes of cancer.[80] However, detection of CTCs has been technically challenging due to the extremely low abundance of CTCs among a large number of blood cells (109 mL). Previous studies have demonstrated that 3D nanostructured (i.e., silicon nanopillar array) substrates coated with cancer-cell capture agents exhibit significantly improved cell-capture efficiency owing to the enhanced topographic interactions between the silicon nanopillars and the nanoscaled cellular surface components (e.g. microvilli and filopodia).[81] Taking into consideration the advantages of fractal nanostructures, we developed FAuNS-based biointerfaces[27] that achieved a dramatic en-

ising, sandwich complexes need multiple steps for their preparation, which thus complicates the analysis processes, and therefore, they are far from meeting the requirements of clinical use. To develop a simple analysis system, Zhang and co-workers reported a fractal Au nanostructure based ultrasensitive aptasensor for thrombin detection with a femtomolar detection limit.[21] Figure 8 a shows the fabrication of the aptasensor and detection of thrombin. Fractal Au nanostructures (Figure 8 b) deposited on an indium tin oxide (ITO) electrode surface were modified with the aptamer as a probe and 6-mercapto-1hexanol (MCH) as a blocking agent. Upon thrombin binding, the formation of an aptamer– thrombin complex perturbed the interfacial electron transfer, and the oxidation peak current of hydroxymethyl ferrocene (C11H12FeO) decreased, which corresponded to the concentration of thrombin in the sample solution. This aptasensor realized Figure 8. a) Scheme for the fabrication and thrombin detection of aptasensor. b) Top-view (left) and side-view label-free and ultrasensitive de- (right) SEM image of the fractal Au nanostructures on the ITO. Reproduced from ref. [21] with permission of The tection of thrombin with a linear Royal Society of Chemistry. Fc = ferrocene.  2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

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hancement in the capture efficiency of MCF7 cells through matching the Df values of the FAuNSs and the cancer cells. The cell-capture efficiency of high FAuNS interfaces is (62  13) %, and these interfaces have a Df value that is similar to that of cancer cells. As a control, the cell-capture efficiency was only (3  1) % for the flat Au interface. Furthermore, the FAuNS interfaces display very low non-specific cell adhesion for the EpCAMnegative cell lines (HeLa, Daudi, and Jurkat T). Additionally, more than 50 % recovery efficiency of spiked MCF7 cells in the wholeblood samples was realized by the FAuNS interfaces, and efficient release of undamaged captured cells was achieved through an electrochemical process; this is indicative of the potential for the early diagnosis and monitoring of cancer in clinics. However, the opaque properties of the above biointerfaces enormously hamper the real- Figure 9. a) The fabrication processes and corresponding SEM/transmission electron microscopy images of the time observation of the process fractal silica nanostructured biointerfaces. b) Transparency of the fractal silica nanostructured biointerfaces in air of detecting cancer cells by mi- and under water. c) Simultaneous CLSM (top) and differential interference contrast (DIC) microscopy (middle) imaging of the captured cells and the merged (bottom) images. Scale bar is 10 mm. Right: imaging the captured croscopy. There is urgent cell with different focal depth along the z axis. Reprinted with permission from ref. [40]. Copyright 2014 Wileydemand for the development of VCH Verlag GmbH & Co. KGaA. underwater-transparent nanostructured biointerfaces that allow the direct bright-field and fluorescence monitoring of ture performance of the biointerface. Moreover, F-actin of cancer cells with high cell-capture efficiency. Recently, a simple MCF7 cells can be visualized at different focal depths by CLSM, way to make robust, transparent, fractal silica nanostructures[82] and the same cell can be imaged by light microscopy. The corresponding merged images indicate that circular-like F-actin is was developed by taking candle soot as the template (Figprojected onto the edge of the cell (Figure 9 c). This underwaure 9 a).[40] First, a layer of candle soot exhibiting a fractal netter transparent biointerface presents a new clue for the develwork was deposited on a quartz substrate. Then, a silica shell opment of multifunctional nanostructured biointerfaces for was coated on the candle-soot template by chemical vapor biomedical applications. deposition (CVD) of silicon tetrachloride. Finally, fractal silica To develop a point-of-care (POC) diagnostic tool for infecnanostructures were obtained through calcination of the soot/ tious diseases, a dendritic nanotip based amperometric biosensilica core–shell nanostructure at 600 8C for 2 h to remove the sor was integrated into a hand-held device for the highly sensitemplate. As shown in Figure 9 b, the fractal silica nanostructive detection of target bacteria (Figure 10 a).[44] The dendritic tured interface is not transparent in air, but it is transparent under water. The transparent biointerface exhibits high cellnanotip, composed of Si nanowires coated with single-walled capture efficiency through enhanced topographic interactions carbon nanotubes (SWCNTs), effectively increased the current between the cancer cells and the fractal nanostructure. Impordensity around the terminal end of the nanotip. The charge tantly, the underwater transparency of the fractal silica nanotransfer on the surface of the SWCNTs decreased with the structured biointerface allows direct monitoring of the capbinding of cells and antibodies, and thus the electric current tured cells by light microscopy in addition to fluorescence midecreased as the cell concentration was increased. The sensicroscopy and confocal laser scanning microscopy (CLSM). Sitivity of the amperometry was 103 cfu mL 1. The captured cells multaneous fluorescence and bright-field imaging of the capon the nanotip were also visible by scanning electron microstured cells can provide insight into the cytomorphologic copy (SEM) and optical and fluorescence microscopy (Figfeatures of CTCs and can further help to evaluate the cell-capure 10 b). The simple configuration of a dendritic nanotip po 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

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Figure 10. a) Conceptual design of a POC hand-held device. b) Optical, SEM, and fluorescence images showing the capture of BCG cells. Bright spots are BCG cells stained by fluorescent antibodies. The circles are the same spots indicating BCG cells. Reproduced from ref. [44] with permission of The Royal Society of Chemistry.

tentially offers an electrolyte-free detection platform for sensitive and rapid biosensors.

4.4. Other Biomarkers Glucose, known as a “silent killer”, is vulnerable to excessive accumulation in the blood of diabetes patients. Thus, it is needed to detect excess amounts of glucose in blood as early as possible and to monitor the concentration over time. Fractal nanostructure based biosensors were developed to meet this clinical demand. Yang and co-workers reported a fractal Ag nanostructured biosensor for the detection of glucose with a linear concentration from 1 to 5 mm by using surface-enhanced Raman scattering (SERS).[58b] Kang and co-workers used a fractal CuNi nanostructured biosensor to detect glucose if the concentration reached at least 4.8  10 5 m by using chronoamperometry techniques.[33] Zhang and co-workers utilized a fractal Ag nanostructure/Cu rod electrode for the detection of two kind of biomolecules over a wide range from 0.2 to 19.2 mm and a low detection limit of 0.1 mm for H2O2 and a linear range from 0.02 to 7.4 mm and optimized detection limit of 0.1 mm for glucose in a short response time (< 3 s).[54] Dopamine, a neurotransmitter, was detected by using a fractal LaFeO3 nanostructure fabricated through a hydrothermal process.[43] The sensor had a response in the linear range from 8.2  10 8 to 1.6  10 7 m with a detection limit of 62 nm and effectively avoided the interference of ascorbic acid and uric acid. Liu and co-workers displayed a fractal nanosilica based immunosensor for the detection of benzo[a]pyrene with linear concentrations between 0.01 and 10 mm and a detection limit of 8 nm.[72] A H2O2 biosensor was developed by using fractal Pd nanostructures. This sensor detected H2O2 in a linear range from 1.0 mm to 1.02 mm with a detection limit of 2.4  10 7 m.[49b]  2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

Fractal structures can provide a unique “fractal contact mode” that ensures an outward, 3D, and spatial contact for biomarkers, a reduced diffusion length for small biomarkers to the surface of the sensor, and enhanced topographic interaction by matching the fractal dimensions of the tumor cells, which makes biomarkers easily accessible to probes and consequently promotes detective efficiency and sensitivity. Fractal nanostructured biointerfaces have been proven to be effective for the ultrasensitive detection of various biomarkers with extremely low abundances in clinical samples, and this paves the way for the further exploitation of these nanostructures in biomedical applications. Moreover, the successful detection of disease-relevant biomarkers, such miRNA, CA-125, and MCF7 cells, from unpurified cell lysates and from the blood of patients makes these nanostructures promising for practical clinical use. Although significant progress has been achieved in this field, the study of fractal nanostructured biointerfaces is still at an early stage and a lot of fundamental and practical problems remain unsolved. In our opinion, future research should mainly focus on three aspects. The first direction is to develop more fractal nanostructured biointerfaces for detecting more disease-related biomarkers, such as viruses.[83] Second, the development of highly desirable automatic hand-held devices or systems, wherein sample preparation, target detection, and result readouts occur within a single system over a short interval, is still requested. Finally, fractal structured systems, such as drug-delivery systems and artificial blood vessels and tissues, should be thoroughly considered. We believe that novel fractal structure based biointerfaces will be in forefront of in vitro healthcare techniques and will greatly benefit the health of humans in the near future.

Acknowledgements We thank the National Research Fund for Fundamental Key Projects (2012CB933800), the National Natural Science Foundation (21121001, 21127025, 21175140, and 20974113), the Key Research Program of the Chinese Academy of Sciences (KJZD-EW-M01), the National High Technology Research and Development Program of China (863 Program) (2013AA031903), and the National Instrumentation Program (NIP) (2013YQ190467) for financial support. Keywords: biointerfaces · biomarkers · detection · fractals · nanostructures [1] T. Soukka, J. Paukkunen, H. Hrm, S. Lçnnberg, H. Lindroos, T. Lçvgren, Clin. Chem. 2001, 47, 1269 – 1278. [2] R. de La Rica, M. M. Stevens, Nat. Nanotechnol. 2012, 7, 821 – 824. [3] V. Ambros, Nature 2004, 431, 350 – 355. [4] M. S. Nicoloso, R. Spizzo, M. Shimizu, S. Rossi, G. A. Calin, Nat. Rev. Cancer 2009, 9, 293 – 302. [5] V. K. Gangaraju, H. Lin, Nat. Rev. Mol. Cell Biol. 2009, 10, 116 – 125. [6] B. R. Cullen, Nature 2009, 457, 421 – 425. [7] a) G. Acharya, C.-L. Chang, D. D. Doorneweerd, E. Vlashi, W. A. Henne, L. C. Hartmann, P. S. Low, C. A. Savran, J. Am. Chem. Soc. 2007, 129, 15824 – 15829; b) E. V. S. Høgdall, L. Christensen, S. K. Kjaer, J. Blaakaer,

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[8] [9]

[10] [11]

[12]

[13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26]

[27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39]

A. Kjærbye-Thygesen, S. Gayther, I. J. Jacobs, C. K. Høgdall, Gynecol. Oncol. 2007, 104, 508 – 515. a) J. V. Tricoli, M. Schoenfeldt, B. A. Conley, Clin. Cancer Res. 2004, 10, 3943 – 3953; b) M. Ferrari, Nat. Rev. Cancer 2005, 5, 161 – 171. a) N. L. Rosi, C. A. Mirkin, Chem. Rev. 2005, 105, 1547 – 1562; b) Y. Zhang, Y. Guo, Y. Xianyu, W. Chen, Y. Zhao, X. Jiang, Adv. Mater. 2013, 25, 3802 – 3819. a) R. M. Crooks, ChemPhysChem 2001, 2, 644 – 654; b) G. Li, X. Li, J. Wan, S. Zhang, Biosens. Bioelectron. 2009, 24, 3281 – 3287. a) J. J. Lundquist, E. J. Toone, Chem. Rev. 2002, 102, 555 – 578; b) J. H. Myung, K. A. Gajjar, J. Saric, D. T. Eddington, S. Hong, Angew. Chem. 2011, 123, 11973 – 11976; Angew. Chem. Int. Ed. 2011, 50, 11769 – 11772; c) C. Fasting, C. A. Schalley, M. Weber, O. Seitz, S. Hecht, B. Koksch, J. Dernedde, C. Graf, E.-W. Knapp, R. Haag, Angew. Chem. 2012, 124, 10622 – 10650; Angew. Chem. Int. Ed. 2012, 51, 10472 – 10498. a) M. Mammen, S.-K. Choi, G. M. Whitesides, Angew. Chem. 1998, 110, 2908 – 2953; Angew. Chem. Int. Ed. 1998, 37, 2754 – 2794; b) C. C. Lee, J. A. MacKay, J. M. J. Frechet, F. C. Szoka, Nat. Biotechnol. 2005, 23, 1517 – 1526; c) J. Satija, V. V. R. Sai, S. Mukherji, J. Mater. Chem. 2011, 21, 14367 – 14386. B. B. Mandelbrot, The Fractal Geometry of Nature, Freeman, New York, 1982, p 6. a) G. B. West, J. H. Brown, B. J. Enquist, Science 1997, 276, 122 – 126; b) N. Williams, Science 1997, 276, 34. U. Frey, A. Hislop, M. Silverman, Respir. Physiol. Neurobiol. 2004, 139, 179 – 189. J. W. Baish, R. K. Jain, Cancer Res. 2000, 60, 3683 – 3688. E. Fernndez, J. A. Bolea, G. Ortega, E. Louis, J. Neurosci. Methods 1999, 89, 151 – 157. a) G. K. Reznik, Environ. Health Perspect. 1990, 85, 171; b) C. Thamrin, G. Stern, U. Frey, Paediatr. Respir. Rev. 2010, 11, 123 – 131. D. H. Werner, P. I. Werner, K. H. Church, Electron. Lett. 2001, 37, 11501151. T. Stergiopoulos, I. M. Arabatzis, H. Cachet, P. Falaras, J. Photochem. Photobiol. A 2003, 155, 163-170. L.-P. Xu, S. Wang, H. Dong, G. Liu, Y. Wen, S. Wang, X. Zhang, Nanoscale 2012, 4, 3786 – 3790. K. B. Cederquist, S. O. Kelley, Curr. Opin. Chem. Biol. 2012, 16, 415 – 421. L. Soleymani, Z. Fang, E. H. Sargent, S. O. Kelley, Nat. Nanotechnol. 2009, 4, 844 – 848. X. Bin, E. H. Sargent, S. O. Kelley, Anal. Chem. 2010, 82, 5928 – 5931. H. D. Hill, J. E. Millstone, M. J. Banholzer, C. A. Mirkin, ACS Nano 2009, 3, 418 – 424. a) M. E. Dokukin, N. V. Guz, R. M. Gaikwad, C. D. Woodworth, I. Sokolov, Phys. Rev. Lett. 2011, 107, 028101; b) S. Iyer, R. M. Gaikwad, V. S. Rao, C. D. Woodworth, I. Sokolov, Nat. Nanotechnol. 2009, 4, 389 – 393; c) A. Mashiah, O. Wolach, J. Sandbank, O. Uziel, P. Raanani, M. Lahav, Acta Haematol. 2008, 119, 142 – 150. P. Zhang, L. Chen, T. Xu, H. Liu, X. Liu, J. Meng, G. Yang, L. Jiang, S. Wang, Adv. Mater. 2013, 25, 3566 – 3570. M. Matsushita, M. Sano, Y. Hayakawa, H. Honjo, Y. Sawada, Phys. Rev. Lett. 1984, 53, 286 – 289. G. L. M. K. S. Kahanda, X.-q. Zou, R. Farrell, P.-z. Wong, Phys. Rev. Lett. 1992, 68, 3741 – 3744. X. Zhang, F. Shi, X. Yu, H. Liu, Y. Fu, Z. Wang, L. Jiang, X. Li, J. Am. Chem. Soc. 2004, 126, 3064 – 3065. N. Zhao, F. Shi, Z. Wang, X. Zhang, Langmuir 2005, 21, 4713 – 4716. L Qian, X. Yang, J. Phys. Chem. B 2006, 110, 16672 – 16678. R. Qiu, X. L. Zhang, R. Qiao, Y. Li, Y. I. Kim, Y. S. Kang, Chem. Mater. 2007, 19, 4174 – 4180. F. Shi, Y. Song, J. Niu, X. Xia, Z. Wang, X. Zhang, Chem. Mater. 2006, 18, 1365 – 1368. Y. Qin, Y. Song, N. Sun, N. Zhao, M. Li, L. Qi, Chem. Mater. 2008, 20, 3965 – 3972. W. Ye, Y. Chen, F. Zhou, C. Wang, Y. Li, J. Mater. Chem. 2012, 22, 18327 – 18334. A. Mohanty, N. Garg, R. Jin, Angew. Chem. 2010, 122, 5082 – 5086; Angew. Chem. Int. Ed. 2010, 49, 4962 – 4966. S. H. Han, L. S. Park, J.-S. Lee, J. Mater. Chem. 2012, 22, 20223 – 20231. C. M. Pandey, G. Sumana, B. D. Malhotra, Biomacromolecules 2011, 12, 2925 – 2932.

 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

www.chemphyschem.org [40] G. Yang, H. Liu, X. Liu, P. Zhang, C. Huang, T. Xu, L. Jiang, S. Wang, Adv. Healthcare Mater. 2014, 3, 332 – 337. [41] K. G. M. Laurier, M. Poets, F. Vermoortele, G. D. Cremer, J. A. Martens, H. Uji-i, D. E. De Vos, J. Hofkens, M. B. J. Roeffaers, Chem. Commun. 2012, 48, 1559 – 1561. [42] R. Wang, D. Liu, Z. Zuo, Q. Yu, Z. Feng, H. Liu, X. Xu, J. Mater. Chem. 2012, 22, 2410 – 2418. [43] S. Thirumalairajan, K. Girija, V. Ganesh, D. Mangalaraj, C. Viswanathan, N. Ponpandian, Cryst. Growth Des. 2013, 13, 291 – 302. [44] J.-H. Kim, M. Hiraiwa, H.-B. Lee, K.-H. Lee, G. A. Cangelosi, J.-H. Chung, RSC Adv. 2013, 3, 4281 – 4287. [45] a) E. Budevski, G. Staikov, W. J. Lorenz, Electrochim. Acta 2000, 45, 2559 – 2574; b) M. Datta, D. Landolt, Electrochim. Acta 2000, 45, 2535 – 2558. [46] L. Soleymani, Z. Fang, X. Sun, H. Yang, B. J. Taft, E. H. Sargent, S. O. Kelley, Angew. Chem. 2009, 121, 8609 – 8612; Angew. Chem. Int. Ed. 2009, 48, 8457 – 8460. [47] X. Qin, Z. Miao, Y. Fang, D. Zhang, J. Ma, L. Zhang, Q. Chen, X. Shao, Langmuir 2012, 28, 5218 – 5226. [48] X. Xu, J. Jia, X. Yang, S. Dong, Langmuir 2010, 26, 7627 – 7631. [49] a) G. T. Duan, W. P. Cai, Y. Y. Luo, Z. G. Li, Y. Li, Appl. Phys. Lett. 2006, 89, 211905; b) P. Zhou, Z. H. Dai, M. Fang, X. H. Huang, J. C. Bao, J. F. Gong, J. Phys. Chem. C 2007, 111, 12609 – 12616; c) T. Huang, F. Meng, L. Qi, Langmuir 2010, 26, 7582 – 7589; d) L. Wang, Y. Yamauchi, J. Am. Chem. Soc. 2009, 131, 9152 – 9153; e) L. Yang, S. Luo, F. Su, Y. Xiao, Y. Chen, Q. Cai, J. Phys. Chem. C 2010, 114, 7694 – 7699; f) H. Chen, P. Kannan, L. Guo, H. Chen, D.-H. Kim, J. Mater. Chem. 2011, 21, 18271 – 18278. [50] H. Martn, P. Carro, A. H. Creus, S. Gonzlez, R. C. Salvarezza, A. J. Arvia, Langmuir 1997, 13, 100 – 110. [51] D. K. Sharma, A. Ott, A. P. O’Mullane, S. K. Bhargava, Colloids Surf. A 2011, 386, 98 – 106. [52] L. Qian, X. Yang, Colloids Surf. A 2008, 317, 528 – 534. [53] X. Xia, Y. Wang, A. Ruditskiy, Y. Xia, Adv. Mater. 2013, 25, 6313 – 6333. [54] X. J. Zhang, R. Ji, L. L. Wang, L. T. Yu, J. Wang, B. Y. Geng, G. F. Wang, CrystEngComm 2013, 15, 1173 – 1178. [55] a) J. P. Bravo-Vasquez, H. Fenniri, J. Phys. Chem. C 2009, 113, 12897 – 12900; b) K. Drozdowicz-Tomsia, F. Xie, E. M. Goldys, J. Phys. Chem. C 2010, 114, 1562 – 1569. [56] C.-Y. Chen, C. P. Wong, Nanoscale 2013, 6, 811 – 816. [57] S. Xie, X. Zhang, D. Xiao, M. C. Paau, J. Huang, M. M. F. Choi, J. Phys. Chem. C 2011, 115, 9943 – 9951. [58] a) R. A. W. Dryfe, E. C. Walter, R. M. Penner, ChemPhysChem 2004, 5, 1879 – 1884; b) X. Wen, Y.-T. Xie, W. C. Mak, K. Y. Cheung, X.-Y. Li, R. Renneberg, S. Yang, Langmuir 2006, 22, 4836 – 4842. [59] L. Soleymani, Z. Fang, S. O. Kelley, E. H. Sargent, Appl. Phys. Lett. 2009, 95, 143701 – 143703. [60] H. Yang, A. Hui, G. Pampalakis, L. Soleymani, F.-F. Liu, E. H. Sargent, S. O. Kelley, Angew. Chem. 2009, 121, 8613 – 8616; Angew. Chem. Int. Ed. 2009, 48, 8461 – 8464. [61] F. Li, X. Han, S. Liu, Biosens. Bioelectron. 2011, 26, 2619 – 2625. [62] W. Sun, X. Qi, Y. Zhang, H. Yang, H. Gao, Y. Chen, Z. Sun, Electrochim. Acta 2012, 85, 145 – 151. [63] L. Shi, Z. Chu, Y. Liu, W. Jin, X. Chen, Biosens. Bioelectron. 2013, 49, 184 – 191. [64] W. Lu, L. Lin, L. Jiang, Biosens. Bioelectron. 2007, 22, 1101 – 1105. [65] L. Soleymani, Z. Fang, B. Lam, X. Bin, E. Vasilyeva, A. J. Ross, E. H. Sargent, S. O. Kelley, ACS Nano 2011, 5, 3360 – 3366. [66] B. Lam, Z. Fang, E. H. Sargent, S. O. Kelley, Anal. Chem. 2012, 84, 21 – 25. [67] Z. Fang, L. Soleymani, G. Pampalakis, M. Yoshimoto, J. A. Squire, E. H. Sargent, S. O. Kelley, ACS Nano 2009, 3, 3207 – 3213. [68] E. Vasilyeva, B. Lam, Z. Fang, M. D. Minden, E. H. Sargent, S. O. Kelley, Angew. Chem. 2011, 123, 4223 – 4227; Angew. Chem. Int. Ed. 2011, 50, 4137 – 4141. [69] J. Das, S. O. Kelley, Anal. Chem. 2011, 83, 1167 – 1172. [70] J. Tang, D. Tang, R. Niessner, D. Knopp, G. Chen, Anal. Chim. Acta 2012, 720, 1 – 8. [71] H. Chen, Z. Gao, Y. Cui, G. Chen, D. Tang, Biosens. Bioelectron. 2013, 44, 108 – 114. [72] C. Wang, M. Lin, Y. Liu, H. Lei, Electrochim. Acta 2011, 56, 1988 – 1994. [73] a) P. E. Sheehan, L. J. Whitman, Nano Lett. 2005, 5, 803 – 807; b) P. R. Nair, M. A. Alam, Nano Lett. 2008, 8, 1281 – 1285.

ChemPhysChem 0000, 00, 1 – 13

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CHEMPHYSCHEM MINIREVIEWS [74] T. M. Squires, R. J. Messinger, S. R. Manalis, Nat. Biotechnol. 2008, 26, 417 – 426. [75] a) R. Schumacher, Angew. Chem. 1990, 102, 347 – 361; Angew. Chem. Int. Ed. Engl. 1990, 29, 329 – 343; b) C. I. Cheng, Y.-P. Chang, Y.-H. Chu, Chem. Soc. Rev. 2012, 41, 1947 – 1971. [76] a) K. S. Novoselov, A. K. Geim, S. V. Morozov, D. Jiang, Y. Zhang, S. V. Dubonos, I. V. Grigorieva, A. A. Firsov, Science 2004, 306, 666 – 669; b) S. Stankovich, D. A. Dikin, G. H. Dommett, K. M. Kohlhaas, E. J. Zimney, E. A. Stach, R. D. Piner, S. T. Nguyen, R. S. Ruoff, Nature 2006, 442, 282 – 286; c) A. K. Geim, K. S. Novoselov, Nat. Mater. 2007, 6, 183 – 191. [77] a) D. A. Tuveson, J. P. Neoptolemos, Cell 2012, 148, 21 – 23; b) N. Erez, Nature 2013, 500, 37 – 38. [78] J. R. Heath, M. E. Davis, Annu. Rev. Med. 2008, 59, 251 – 265.

 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim

www.chemphyschem.org [79] R. A. Sellwood, S. W. A. Kuper, J. I. Burn, E. N. Wallace, Br. Med. J. 1964, 1, 1683 – 1686. [80] M. Cristofanilli, G. T. Budd, M. J. Ellis, A. Stopeck, J. Matera, M. C. Miller, J. M. Reuben, G. V. Doyle, W. J. Allard, L. W. M. M. Terstappen, D. F. Hayes, New Engl. J. Med. 2004, 351, 781 – 791. [81] S. Wang, H. Wang, J. Jiao, K.-J. Chen, G. E. Owens, K.-i. Kamei, J. Sun, D. J. Sherman, C. P. Behrenbruch, H. Wu, H.-R. Tseng, Angew. Chem. 2009, 121, 9132 – 9135; Angew. Chem. Int. Ed. 2009, 48, 8970 – 8973. [82] X. Deng, L. Mammen, H.-J. Butt, D. Vollmer, Science 2012, 335, 67 – 70. [83] T. T. Wang, P. Palese, Science 2011, 333, 834 – 835. Received: December 30, 2013 Published online on && &&, 2014

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MINIREVIEWS Facing forward: Fractal structures offer a unique “fractal contact mode” that guarantees the efficient working of organisms with an optimized style. Fractal nanostructured biointerfaces show great potential for the ultrasensitive detection of various disease-relevant biomarkers, such as microRNA, cancer antigen 125, and breast cancer cells, from unpurified cell lysates and the blood of patients.

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P. Zhang, S. Wang* && – && Designing Fractal Nanostructured Biointerfaces for Biomedical Applications

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Designing fractal nanostructured biointerfaces for biomedical applications.

Fractal structures in nature offer a unique "fractal contact mode" that guarantees the efficient working of an organism with an optimized style. Fract...
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